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BacHBerry:: BACterial Hosts for production of Bioactive phenolics from bERRY fruits
Dudnik, Alexey; Almeida, A. Filipa; Andrade, Ricardo; Avila, Barbara; Bañados, Pilar; Barbay, Diane; Bassard, Jean Etienne; Benkoulouche, Mounir; Bott, Michael; Braga, Adelaide Total number of authors: 83
Published in: Phytochemistry Reviews
Link to article, DOI: 10.1007/s11101-017-9532-2
Publication date: 2018
Document Version Peer reviewed version
Link back to DTU Orbit
Citation (APA): Dudnik, A., Almeida, A. F., Andrade, R., Avila, B., Bañados, P., Barbay, D., Bassard, J. E., Benkoulouche, M., Bott, M., Braga, A., Breitel, D., Brennan, R., Bulteau, L., Chanforan, C., Costa, I., Costa, R. S., Doostmohammadi, M., Faria, N., Feng, C., ... Forster, J. (2018). BacHBerry:: BACterial Hosts for production of Bioactive phenolics from bERRY fruits. Phytochemistry Reviews, 17(2), 291-326. https://doi.org/10.1007/s11101-017-9532-2
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1 TITLE
2 BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits
3 RUNNING TITLE
4 BacHBerry
5
6 AUTHORS
7 The list of contributing authors is given on p. 2.
8
9 *Corresponding author:
10 Alexey Dudnik
11 Email: [email protected]
12 Phone: + 45 93 51 11 01
13 Fax: +45 45 25 80 01
14
15
16 Keywords: berries; bioprospecting; microbial cell factories; polyphenols; sustainable production.
17
18 Footnote
19 This article is written by the BacHBerry consortium (www.bachberry.eu) and represents the collective effort 20 of all participating institutions. The authors are therefore listed in alphabetical order.
1
21 The list of authors
22 Alexey Dudnik1,*, A. Filipa Almeida15,20, Ricardo Andrade2, Barbara Avila3, Pilar Bañados3, Diane Barbay4,
23 Jean-Etienne Bassard5, Mounir Benkoulouche4, Michael Bott6, Adelaide Braga7, 21, Dario Breitel12, Rex
24 Brennan10, Laurent Bulteau2, Celine Chanforan8, Inês Costa15,20, Rafael S. Costa9, Mahdi
25 Doostmohammadi9,22, Nuno Faria7, 21, Chengyong Feng17, Armando Fernandes9, Patricia Ferreira7, 21, Roberto
26 Ferro1, Alexandre Foito10, Sabine Freitag10, Gonçalo Garcia15,20, Paula Gaspar1, Joana Godinho-Pereira15,20,
27 Björn Hamberger5, András Hartmann9, Harald Heider4, Carolina Jardim15,20, Alice Julien-Laferriere2, Nicolai
28 Kallscheuer6, Wolfgang Kerbe13, Oscar P. Kuipers11, Shanshan Li17, Nicola Love12, Alberto Marchetti-
29 Spaccamela2, Jan Marienhagen6, Cathie Martin12, Arnaud Mary2, Vincent Mazurek8, Camillo Meinhart13,
30 David Méndez Sevillano14, Regina Menezes15,20, Michael Naesby4, Morten H.H. Nørholm1, Finn T. Okkels8,
31 Joana Oliveira7, 21, Marcel Ottens14, Delphine Parrot2, Lei Pei13, Isabel Rocha7, 21, Rita Rosado-Ramos15,20,
32 Caroline Rousseau4, Marie-France Sagot2, Claudia Nunes dos Santos15,20, Markus Schmidt13, Tatiana
33 Shelenga16, Louise Shepherd10, Ana Rita Silva7, 21, Marcelo Henriques da Silva14, Olivier Simon8, Steen
34 Gustav Stahlhut1, Ana Solopova11, Artem Sorokin16, Derek Stewart10,19, Leen Stougie2, Shang Su17, Vera
35 Thole12, Olga Tikhonova16, Martin Trick12, Philippe Vain12, André Veríssimo9, Ana Vila-Santa9, Susana
36 Vinga9, Michael Vogt6, Liangsheng Wang17, Lijin Wang17, Wei Wei17, Sandra Youssef13, Ana Rute Neves18
37 & Jochen Forster1
38
39 1 The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet,
40 Building 220, 2800 Kgs. Lyngby, Denmark
41 2 Institut National de Recherche en Informatique et Automatique, 43 Boulevard du 11 Novembre 1918,
42 69622 Villeurbanne Cedex, France
43 3 Facultad De Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Av. Vicuña
44 Mackenna Ote, 4860 Macul, Chile
45 4 Evolva, Duggingerstrasse 23, 4053 Reinach, Switzerland
46 5 Department of Plant and Environmental Science, University of Copenhagen, 1871 Frederiksberg, Denmark
2
47 6 Forschungszentrum Jülich GmbH, Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie,
48 Wilhelm-Johnen-Straße, 52428 Jülich, Germany
49 7 Biotempo, Avepark – Edif. Spinpark, Zona Industrial da Gandra, Barco, 4805-017 Guimarães, Portugal
50 8 Chr. Hansen Natural Colors A/S, Agern Alle 24, 2970 Hørsholm, Denmark
51 9 IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa,
52 Portugal
53 10 The James Hutton Institute, Invergowrie, DD2 5DA Dundee, Scotland, United Kingdom
54 11 Groningen Biomolecular Sciences and Biotechnology Institute, Department of Molecular Genetics,
55 University of Groningen, Linnaeusborg, Nijenborgh 7, 9747 AG Groningen, The Netherlands
56 12 John Innes Centre, Norwich Research Park, NR4 7UH Norwich, United Kingdom
57 13 Biofaction KG, Kundmanngasse 39/12, 1030 Vienna, Austria
58 14 Department of Biotechnology, Delft University of Technology, van der Maasweg 9, 2629 HZ Delft, The
59 Netherlands
60 15 Instituto de Biologia Experimental e Tecnológica, Av. República, Qta. do Marquês, 2780-157 Oeiras,
61 Portugal
62 16 Fruit Crops Genetic Resources Department, N. I. Vavilov Research Institute of Plant Industry, B.
63 Morskaya Street 42-44, 190000 St. Petersburg, Russia
64 17 Institute of Botany, The Chinese Academy of Sciences, 20 Nanxincun, Xiangshan, 100093 Beijing, China
65 18 Chr. Hansen Holding A/S, Bøge Alle 10-12, 2970 Hørsholm, Denmark
66 19Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences,
67 Heriot Watt University, Edinburgh, Scotland, UK
68 20Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da
69 República, 2780-157 Oeiras, Portugal.
70 21Centre of Biological Engineering, University of Minho, Campus de Gualtar 4710-057, Braga, Portugal
71 22School of Mathematics, University of Edinburgh, EH9 3FD Edinburgh, Scotland, United Kingdom
3
72 Abstract
73 BACterial Hosts for production of Bioactive phenolics from bERRY fruits (BacHBerry) was a three-year
74 project funded by the Seventh Framework Programme (FP7) of the European Union that ran between
75 November 2013 and October 2016. The overall aim of the project was to establish a sustainable and
76 economically-feasible strategy for the production of novel high-value phenolic compounds isolated from
77 berry fruits using bacterial platforms. The project aimed at covering all stages of the discovery and pre-
78 commercialization process, including berry collection, screening and characterization of their bioactive
79 components, identification and functional characterization of the corresponding biosynthetic pathways, and
80 construction of Gram-positive bacterial cell factories producing phenolic compounds. Further activities
81 included optimization of polyphenol extraction methods from bacterial cultures, scale-up of production by
82 fermentation up to pilot scale, as well as societal and economic analyses of the processes. This review article
83 summarizes some of the key findings obtained throughout the duration of the project.
84
4
85 Introduction
86 BacHBerry (BACterial Hosts for production of Bioactive phenolics from bERRY fruits, www.bachberry.eu)
87 has received funding from the European Commission’s Seventh Framework Programme (FP7) under the
88 theme KBBE.2013.3.1-01: “Plant High Value Products - from discovery to final product” with the goal of
89 bringing together experts from the fields of plant biology, industrial biotechnology, analytical chemistry, and
90 social sciences in order to assemble and validate a complete pipeline for discovery and microbial production
91 of novel phenolic compounds of plant origin. The consortium consisted of twelve academic groups, five
92 small and medium sized enterprises and one large enterprise from eleven different countries, namely Austria,
93 Chile, China, Denmark, France, Germany, The Netherlands, Portugal, Russia, Switzerland and the United
94 Kingdom. The diversity of partners has allowed access to a variety of expertise and to plant material that had
95 not been characterized extensively.
96 Polyphenols as a source of bioactive molecules
97 Historically, plants have been not only important sources of nutrients and energy in the human diet but also
98 have provided the fundamentals of traditional and modern medicine. The Plantae kingdom has been
99 estimated to produce more than 200,000 different chemical compounds (Weckwerth 2003), including
100 compounds with proven and potential medical applications. This is reflected in the high number of
101 pharmaceutical products based on, or derived from, plant natural products, such as aspirin, morphine,
102 quinine, paclitaxel and artemisinin (Cragg and Newman 2013). Among the different classes of specialized
103 (i.e. secondary) metabolites phenylpropanoids stand out due to their diversity and ubiquity in the plant
104 kingdom. Fleshy fruits, a rich source of bioactive components, such as ascorbate (vitamin C), terpenoids and
105 polyphenols, are considered to have one the highest antioxidant capacity of commonly eaten foods
106 (Halvorsen et al. 2006). Polyphenol content and composition can vary substantially between fleshy-fruit
107 varieties (Carvalho et al. 2013; Miladinović et al. 2014; Kula et al. 2016), different cultivation conditions
108 (Josuttis et al. 2013; Vagiri et al. 2013; Mazur et al. 2014), harvest times and as a result of different post-
109 harvest factors (Kårlund et al. 2014; Stavang et al. 2015), but particularly between species (Moyer et al.
5
110 2001; McDougall et al. 2008; Ruiz et al. 2010; Marhuenda et al. 2016). Furthermore, plant polyphenols are
111 increasingly being associated with putative bioactivities offering protection against several cardiovascular
112 (Goszcz et al. 2015) and neurological diseases (Figueira et al. in press).
113 The hydroxystilbenes are a group of polyphenols with a C6-C2-C6 skeleton with two or more hydroxyl
114 groups, where a central carbon–carbon double bond is conjugated with two phenolic moieties (Quideau et al.
115 2011; Kasiotis et al. 2013). The most well-known representative of this group is resveratrol (trans-3,5,4’-
116 hydroxystilbene) (Figure 1a). The compound has gained increasing attention with the discovery of the
117 “French paradox”, an observation that despite a diet with relatively high amounts of saturated fats, French
118 people suffer a relatively low incidence of mortality from coronary heart disease (Renaud and de Lorgeril
119 1992; Catalgol et al. 2012). Like other polyphenols, stilbenes can be further decorated by O-methylation (e.g.
120 resulting in 3-methoxy-resveratrol, or pinostilbene, and 3,5’-dimethoxy-resveratrol, or pterostilbene),
121 acylation (hydrangenic acid) or by glycosylation (e.g. piceid and resveratroloside) (Kim et al. 2002; Becker
122 et al. 2003; Zhang et al. 2009; Wang et al. 2015).
123 The flavonoids are one of the largest families of phenolic compounds. Flavonoids are characterized by their
124 basic skeleton composed of three rings (Ghosh and Scheepens 2009), including two benzene rings (A & B)
125 and one heterocyclic ring (C) (Figure 1b). So far, over 5,000 naturally occurring flavonoids have been
126 characterized from various plants and they have been classified into six subgroups, including anthocyanidins
127 (e.g. cyanidin and delphinidin); flavan-3-ols (e.g. catechin and epicatechin); flavonols (e.g. quercetin and
128 kaempferol); flavones (e.g. luteonin and apigenin); isoflavones (e.g. genistein and daidzein), and flavanones
129 (e.g. hesperetin and naringenin).
130 Within each subgroup, considerable variation can exist when it comes to phenolic composition of different
131 fruits, and in particular of berries. Anthocyanin composition provides an excellent example of this diversity:
132 the red-orange color of strawberries is due to the presence of pelargonidin-type anthocyanins in the flesh and
133 skin whereas the deep purple-black color of blackcurrants is due to the accumulation of high amounts of
134 delphinidin and cyanidin-type anthocyanins in the skin.
6
135 Anthocyanidins are flavonoids, which are characterized by a hydroxyl group in position 3 and a C-ring with
136 a positively-charged oxonium ion (Figure 1b). Anthocyanins are water-soluble glycosides of
137 anthocyanidins, in which sugars, consisting of one or more hexoses, are linked to the 3-hydroxyl group of the
138 pyrylium ring (Heldt and Piechulla 2011). These compounds are responsible for the orange/red-to-blue
139 coloration observed in some plants. The most common anthocyanidins include cyanidin (red), delphinidin
140 (bluish-purple), malvinidin (reddish-purple), pelargonidin (orange-red), peonidin (pink) and petunidin
141 (purple). The distribution of anthocyanidins can vary greatly among different berry species (Table 1).
142 Flavonoid compounds frequently are decorated with sugar moieties, sugar-acyl moieties (Giusti and
143 Wrolstad 2003) and can be associated with other flavonoids. The patterns of decoration differ greatly
144 amongst species (see Supplementary Tables S1 and S2).
145 Flavonoids are far more prevalent and diverse in berry species than in other common fruits and vegetables.
146 High flavonol contents are observed in cranberry, lingonberry and blackcurrant (Häkkinen et al. 1999),
147 anthocyanins are the most abundant polyphenol pigments (2-5 g kg-1 fresh weight) in berries (Määttä et al.
148 2001) and many simple phenolic acids are abundant in a wide range of berry species (Herrmann and Nagel
149 1989). Additionally, berries constitute one the most important dietary sources of ellagitannins such as
150 sanguiin H6, lambertianin C (Tö rrö nen 2009; Landete 2011) and condensed tannins such as the
151 proanthocyanidins (Rasmussen et al. 2005; Hellströ m et al. 2009). Stilbenes, in contrast, are not that
152 widespread in berries: resveratrol shows highest abundance in grapes (up to 20-30 mg kg-1 fresh weight), but
153 small amounts of resveratrol, pterostilbene and piceatannol have been also detected in blueberry, cowberry,
154 cranberry, deerberry and lingonberry (Jeandet et al. 1991; Paredes-López et al. 2010; Manganaris et al.
155 2014). It is clear that soft-fruit species contain a staggering diversity of distinct polyphenol derivatives whose
156 potential is yet to be harnessed.
157 The market for polyphenolic compounds has seen substantial growth over the past few years, and is expected
158 to exceed 850 million USD by 2018 (Aranaz et al. 2016). The major factors contributing to this trend are the
159 growing consumer awareness regarding the benefits of polyphenol consumption and the increasing use of
7
160 polyphenol-containing extracts in food, beverage, and cosmetics products particularly in Asia (Jain and
161 Ramawat 2013; Aranaz et al. 2016; Grand View Research Inc. 2016). The increase in demand also requires
162 efficient and eco-friendly production processes, to improve on current manufacturing practices that mostly
163 rely on extraction from various plant sources (e.g. roots, leaves or fruits) via complex downstream
164 processing (Wang et al. 2016a). The BacHBerry consortium decided to address these challenges and set the
165 following objectives: (i) to systematically analyze the phenolic contents in the large berry germplasm
166 collections available from consortium members, (ii) to establish a publicly-available database of
167 transcriptomic and metabolic data obtained from berry bioprospecting within, as well as outside of the
168 project, (iii) to discover novel bioactivities in berry extracts against a range of human pathologies, such as
169 Alzheimer’s disease and Amyotrophic Lateral Sclerosis (ALS), by high-throughput screening with
170 subsequent identification of functional biomolecules, (iv) to identify the corresponding biosynthetic genes
171 and perform functional characterization of the respective gene products, (v) to assay a selection of the
172 biosynthetic genes for functionality in Gram-positive bacterial hosts and use those to construct bacterial cell
173 factories for phenolic production, (vi) to improve the production efficiency further by introducing
174 modifications to the host metabolic networks predicted via rational design or computational tools developed
175 within the project, (vii) to design and optimize cost-effective food-grade methods for extraction of phenolic
176 compounds from bacterial fermentation broth, and (viii) to optimize fermentation conditions and
177 subsequently upscale the production to pilot plant levels.
178 The project commenced with the selection, sampling and whole-metabolite profiling of berries. In order to
179 explore the potential of the phytochemical diversity present in different berry species, we undertook an
180 untargeted characterization of a wide collection of berries from different species/cultivars utilizing
181 metabolomics-based methods to aid the selection of candidate berry extracts for bioactivity screening.
182 Phytochemical diversity in sampled berries
183 Although significant advances have been made in characterizing the polyphenolic complement of berries,
184 particularly in the context of cultivated species and genotypes, there is limited literature available regarding
8
185 the phytochemical composition of wild and underutilized species/cultivars. Indeed, wild berries are
186 commonly reported as particularly rich in diverse phenolic compounds, often as a result of phenolic-
187 associated astringency having been bred out of cultivated species (Häkkinen et al. 1999). The phenolic
188 diversity of individual berries has been highlighted in many publications (Määttä et al. 2003; Zadernowski et
189 al. 2005; Milivojević et al. 2011), but studies that capture this broad diversity systematically are limited. In
190 BacHBerry, we aimed not only to address this knowledge gap but also to go beyond the state-of-the-art by
191 combining analyses of phenolic diversity with functional testing of berry extracts. The genera targeted in this
192 study included Rubus, Ribes, Vaccinium, Lonicera, Lycium, Aristotelia, Berberis and Ugni collected from
193 different locations in the world including Chile, China, Portugal, Russia and United Kingdom (see
194 Supplementary Table S3).
195 Berry extract metabolite profiling
196 Fruit samples from a total of 112 species/genotypes were collected, extracted, and subsequently subjected to
197 liquid-chromatography mass-spectrometry (LC-MS) metabolomic analysis (see details in Supplementary
198 Materials S1). An untargeted method was used to generate a total of 1,890 mass spectral features (1,506 and
199 384 in positive and negative modes, respectively), which were integrated to generate the dataset used for
200 statistical analysis. Principal component analysis (PCA) was used as a multivariate statistical analysis tool to
201 identify those species, which had the most distinct phytochemical profiles (Figures 2a and 2b).
202 Using the four principal components it was possible to select the species/genotypes, which represented the
203 broadest phenolic diversity (Table 2). In addition to selecting the outlier groups of species, it was also
204 decided to include a small subset of samples (two Rubus idaeus genotypes), which did not separate from the
205 majority of samples in the first principal components. This provided samples with phenolic profiles distinct
206 from the outlier samples and standards for comparison between uncultivated/underutilized species and
207 commonly grown species; in essence a commercial control. The fruits from the selected species (Table 2)
208 were extracted and tested for bioactivity in yeast (Saccharomyces cerevisiae) models for pathological
209 processes associated with several chronic disorders.
9
210 Berry extract bioactivity screening
211 Evidence for the protective role of polyphenols against chronic diseases has increased over the past 20 years
212 (Figueira et al. in press; Goszcz et al. 2015). Neurodegenerative diseases (NDs) represent a group of chronic
213 neurological conditions affecting millions of people worldwide, among which are the Alzheimer's Disease
214 (AD), the Parkinson's Disease (PD), the Huntington's Disease (HD), and Amyotrophic Lateral Sclerosis
215 (ALS). All these diseases have in common the aggregation and deposition of protein aggregates, namely of
216 amyloid β42 (Aβ42) (O’Brien and Wong 2011), αSynuclenin (αSyn) (Shults 2006), huntingtin (HTT)
217 (Miller-Fleming et al. 2008) and FUsed in Sarcoma (FUS) (Kwiatkowski et al. 2009; Vance et al. 2009),
218 which represent the pathological hallmarks of AD, PD, HD and ALS, respectively. In addition, chronic
219 activation of innate immune responses is a process closely associated with neurodegeneration. Its modulation
220 is driven by persistent activation of key transcription factors, such as the Nuclear Factor of Activated T-cells
221 (NFAT) and Nuclear Factor κB (NFκB), which upregulate pro-inflammatory responses creating a positive
222 feedback loop further amplifying initial stimuli. It has been argued that disruption of this loop may represent
223 an important strategy to mitigate the progression of NDs. The pleiotropic effects of polyphenols have been
224 shown to interfere with aggregation-driven neurodegeneration as well as to attenuate chronic inflammatory
225 processes, thereby improving health of cellular and animal models (Figueira et al, in press). Consequently,
226 polyphenol-based therapies represent an underexplored strategy to minimize the huge social and economic
227 impact of NDs.
228 The high degree of evolutionary conservation of fundamental biological processes among eukaryotes has
229 established yeast as a validated model organism to decipher the intricacies of human pathologies, particularly
230 NDs, to identify molecular targets amenable to therapeutic intervention as well as lead molecules with
231 health-promoting potential (Kritzer et al. 2009; Su et al. 2010; Tardiff et al. 2012; Menezes et al. 2015).
232 BacHBerry aimed at identifying phenolic bioactives from harnessing the diversity of phenolics in selected
233 berry germplasm from cultivated, wild and underutilized species of berries. The yeast-based screening
234 platform for bioactivity identification comprised a set of genetically modified S. cerevisiae strains expressing
235 Green Fluorescent Protein (GFP) fused with Aβ42 (Bharadwaj et al. 2010), αSyn (Outeiro and Lindquist
10
236 2003), HTT (Krobitsch and Lindquist 2000) and FUS (Ju et al. 2011) under the control of a galactose-
237 inducible promoter (Figure 3a). Upon activation of expression, these proteins start forming aggregates,
238 which consequently has a negative impact on cellular growth and results in lower GFP fluorescence levels.
239 In case an extract possesses bioactivity against one of the diseases, addition of the extract to the activated
240 yeast cells reduces growth inhibition in the corresponding model. These yeast strains accelerated the
241 identification of phenolic compounds with health-promoting attributes among the most chemically diverse
242 samples identified by the metabolomic analysis (Table 2). For information on the procedures used during the
243 screening see Supplementary Materials S1.
244 An illustrative example of how bioactivities for AD were identified is given in Figure 4a. Upon shift of
245 cells to galactose medium to induce expression of GFP-Aβ42, yeast growth was impaired in comparison to
246 the control strain indicating GFP-Aβ42 proteotoxicity. The treatment with polyphenol-enriched extracts of
247 Lycium chinense significantly recovered growth of these cells revealing its protective role via modulation of
248 Aβ42 toxicity. The bioactivities for PD, HD and ALS were screened using a similar approach, in cells
249 expressing the respective disease proteins.
250 In addition, we have also used a yeast-based model for inflammatory signaling that is based on a Crz1
251 reporter-strain (Prescott et al. 2012; Garcia et al. 2016) (Figure 3b). Crz1 is a yeast orthologue of NFAT,
252 which is an important modulator of inflammation in humans that is known to be involved in development of
253 multiple disorders, such as the inflammatory bowel disease or the rheumatoid arthritis (Pan et al. 2013). Both
254 Crz1 and NFAT are known to be activated by a serine/threonine protein phosphatase calcineurin (CaN) in a
255 Ca2+-dependent manner (Rusnak and Mertz 2000; Bodvard et al. 2013). The utilized reporter strain encodes
256 the β-galactosidase gene (lacZ) under the control of a promoter bearing Crz1-binding sites, the Calcineurin-
257 Dependent Responsive Element (CDRE), allowing the assessment of Crz1 activation through the
258 measurement of β-galactosidase activity using chromogenic substrates (Garcia et al. 2016). Given the
259 evolutionary conservation of NFAT and Crz1 activation mechanisms, in combination with the conserved
260 activity of the immunosuppressant FK506 both in yeast and in humans, the yeast Ca2+/CaN/Crz1 reporter
261 assay represents a reliable tool to identify bioactives with potential to attenuate NFAT-mediated
11
262 inflammatory responses. The potential of Lycium chinense polyphenol-enriched extracts to attenuate
263 inflammation is shown in Figure 4b, exemplifying the approach used in BacHBerry to filter for potential
264 anti-inflammatory activities. The activation of Crz1 by MnCl2, mimicking NFAT activation, led to high β-
265 galactosidase activity and cell treatment with FK506 and Lycium chinense extract strongly reduced the β-
266 galactosidase levels revealing its ability to modulate Crz1, and potentially NFAT, activation.
267 These examples illustrate the strategy used in the BacHBerry project to search for bioactive compounds
268 interfering with pathological processes of NDs and inflammation. The yeast-based screening platform also
269 included strains allowing the identification of metabolites with potential application for type II diabetes,
270 hematological diseases and cancer (unpublished results).
271 Bioassay-guided fractionation compound discovery and candidate compound validation
272 This method of discovering new bioactive natural products ultimately depends on the availability of
273 biological material and preparative- or semi-preparative-scale analytical methods with the capability of
274 resolving mixtures of different classes of compounds typically present in berry extracts (Pauli et al. 2012).
275 Semi-preparative chromatography was used to fractionate selected berry extracts with potential bioactive
276 properties using a hybrid approach of bioassay-guided fractionation procedure (Yang et al. 2001; Weller and
277 G. 2012; Tayarani-Najaran et al. 2013) and screening of pure compounds (Watts et al. 2010). Bioassay-
278 guided fractionation typically involves the following steps: assessment of bioactivity, extraction of the
279 biological material with different solvents and testing of bioactivity. Once bioactivity of an extract has been
280 validated, the extract gets subjected to an iterative process of sub-fractionation/bioactivity testing until pure
281 bioactive natural products are obtained for structural characterization. This approach benefits from exclusion
282 of extracts that do not have bioactivity. However, this procedure requires extensive use of biological material
283 and expensive materials and may result in the isolation of already-known natural products (Duarte et al.
284 2012). Another disadvantage is that the approach is based on the assumption that bioactivity is conferred by
285 a pure compound, although this method can also be used for identification of bioactivities conferred by a
286 cumulative interactions of several polyphenols. Alternatively, pure-compound screening relies on an initial
12
287 isolation and structural elucidation of the individual compounds present in the biological extract followed by
288 bioassay screening. This strategy allows the researcher to focus solely on novel compounds, without re-
289 discovering compounds with well-annotated bioactivity. However, it may also lead to the identification of
290 novel compounds with no bioactivity (Duarte et al. 2012). The second method misses any synergistic
291 interactions affecting bioactivities of berry phenolics.
292 The limited amount of biological material available restricted the number of iterations of fractionation and
293 re-testing of fraction bioactivity possible for typical bioassay-guided fractionation approaches. As described
294 previously, berry extracts typically comprise a relatively diverse and large pool of metabolites that surpass
295 by far the throughput capability of the bioactivity assays used in this study. To overcome these challenges, a
296 hybrid approach was adopted which consisted of several steps: (i) assessment of potential bioactivity present
297 in extracts (as described above), (ii) fractionation of bioactive extracts (see Supplementary Materials S1),
298 (iii) assessment of potential bioactivity present in fractions, (iv) mass-spectrometry-based chemical
299 characterization of bioactive fractions, (v) bioactivity testing of pure candidate compounds (Figure 5).
300 Although this approach shared some of the limitations of the other approaches, it did allow for the exclusion
301 of non-bioactive biological extracts or fractions and focused on the identification novel compounds with
302 potential bioactivities with limited requirement for biological material.
303 Following fractionation of the most promising berry extracts from the first round of screening using the
304 yeast-based platform, isolated fractions were re-analyzed in order to detect bioactive fractions. Subsequently,
305 metabolomic analyses were used for the identification of the individual compounds in each of the bioactive
306 fractions. By utilizing this approach, a set of candidate compounds was defined and re-tested as pure
307 compounds in the yeast-based platform, with further bioactivity validation in the mammalian cell models.
308 Following the BacHBerry pipeline, this information then served as starting point for the engineering of the
309 synthetic pathway for biosynthesis of one of the validated compounds in food-grade bacteria and its
310 subsequent production in small-scale fermenters. The final step of the pipeline, namely re-confirmation of
311 bioactivity of microbially-produced compounds extracted from fermentation broth in both yeast and and
312 mammalian cell models, is currently underway.
13
313 Identification of metabolic pathways and regulators involved in phenolic production
314 The next step within the BacHBerry pipeline involved the identification of the biosynthetic genes and
315 pathways corresponding to the newly discovered bioactives, in order to establish production of these
316 compounds in bacterial hosts. In plants, the biosynthesis of polyphenols occurs via the phenylpropanoid
317 pathway, where the aromatic amino acid L-phenylalanine serves as a precursor. Depending on the enzyme
318 combination, several compounds can be produced in bacteria, including cinnamic acid derivatives, lignin
319 subunits, lignans, phenylpropenes and coumarins, all sharing a basic C6-C3 skeleton (Vogt 2010).
320 Furthermore, the phenylpropanoid backbone can be extended with up to three acetyl-units derived from
321 malonyl-CoA by chalcone synthase (CHS) or stilbene synthase (STS), polyketide synthases which generate
322 various polyketides, such as flavonoids and stilbenes. The core pathways of phenolic biosynthesis are very
323 well understood, with common enzymes, such as, phenylalanine ammonia lyase (PAL), cinnamate 4-
324 hydroxylase (C4H), p-coumaroyl CoA ligase (4CL), hydroxycinnamoyl-CoA shikimate/quinate
325 hydroxycinnamoyltransferase (HCT), p-coumaroyl shikimate/quinate 3'-hydroxylase (C3’H), stilbene
326 synthase and chalcone synthase, found in many plant species (Figure 6).
327 Phenolic chemodiversity in berries, as in other plants, derives from a variety of decorations to the backbone
328 of phenolic structures that include hydroxylations, O-methylations, prenylations, aryl migrations,
329 glycosylations, acylations and polymerizations. For example, anthocyanins are glycosylated on 3-OH
330 positions of the C-ring (Figure 1) and many harbor additional glycosyl groups (for example 5’-O-
331 glycosylation and 3’-O-glycosylation), methoxy groups (petunidin and malvinidin), or hydroxyl groups
332 (which differentiate pelargonidin, cyanidin and delphinidin (Figure 6 and Table 1)). These decorations may
333 affect the bioavailability, bioactivity and stability of polyphenolic compounds. It is therefore essential to
334 develop a greater knowledge of the decorating enzymes specific to berry bioactives, such as those
335 differentially decorating anthocyanins, flavonols, stilbenes, or condensed tannins. Many genes encoding
336 enzymes involved in flavonol and anthocyanin decoration have already been characterized not only in
337 planta, but also heterologously in microorganisms, such as Escherichia coli and S. cerevisiae (Tohge et al.
338 2005; Luo et al. 2007; Pandey et al. 2016; Wang et al. 2016a).
14
339 Berry bioactives identified within the project had special decorations, and those where the genes encoding
340 the decorating activities had not been previously identified, required further research. In a recent work, the
341 transcriptome of the commercial blueberry (Vaccinium corymbosum) was studied using RNA sequencing
342 technology (RNA-seq) in order to discover putative genes related to antioxidant production using next-
343 generation sequencing (NGS) (Li et al. 2012). Similarly, we aimed to identify decorating enzymes and
344 regulators of the biosynthetic pathways of berry bioactives by obtaining transcriptome sequences of target
345 species at different stages of fruit development. This information was used to construct a database of
346 transcriptome sequences for a broad range of berry species, especially those identified as target species
347 containing the promising bioactive molecules. In addition, the overall production of polyphenols is regulated
348 by a range of transcription factors and these were also investigated within the BacHBerry project, with the
349 objective of identifying markers for breeding enhanced levels of phenolic bioactives using the diverse
350 germplasm in selected berry species.
351 Firstly, a library of reference sequences consisting of plant gene sequences encoding enzymes of the core
352 phenolic biosynthetic pathways, which was subsequently used for BLAST searches of berry transcriptomes,
353 was compiled. This library included well-known and experimentally validated anthocyanin, flavonol,
354 stilbene and catechin biosynthetic enzymes from a wide range of plant species. Next, 13 germplasms
355 spanning eight genera, seven families and seven orders were selected for RNA-seq of berry transcriptomes.
356 Sequencing of selected berries at different stages of maturation was also conducted. The data have provided
357 significant resources not only for understanding phenolic biosynthesis in berries, but also for investigating
358 commonalities in genes expressed in berries (Table 3).
359 Multi-layer comparisons of the metabolomics and transcriptomics data of developmental stages, and
360 targeted, as well as, non-targeted searches of candidate transcripts were performed to elucidate genes
361 involved in specific phenolics biosynthetic pathways. Analyses of expression of these genes from different
362 developmental stages of selected berry species were performed with the expVIP tool (Borrill et al. 2016).
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363 These data were used to search for genes encoding putative decorating enzymes based on the list of
364 molecules of potential interest as bioactives and having special decorations in target berry species.
365 Candidates were identified mostly by searching for homologues of well-characterized plant genes. This was
366 carried out with the help of MassBlast, a software developed within the BacHBerry project
367 (https://github.com/averissimo/mass-blast; (Veríssimo et al. submitted)). We annotated all major enzyme
368 families, including cytochromes P450 (CYP), 2-oxoglutarate-dependent dioxygenases (2-OGDs), UDP-
369 glycosyltransferases (UGTs), BAHD acyltransferases, and O-methyltransferases (OMTs), in all 13 selected
370 berry germplasms. Average counts per transcriptome are presented in Table 4. We also detected candidate
371 transcript(s) for 60-80% of the known biochemical steps of the phenylpropanoid pathway, representing
372 between 109-180 candidates per transcriptome. Approximately 40% of these candidates identified from the
373 transcriptomes were full-length sequences. Furthermore, biochemical activities of some of enzymes encoded
374 by the candidate genes were tested.
375 We also looked for transcripts encoding regulators of phenolic biosynthesis. Since the R2R3-MYB gene
376 family operates extensively in controlling phenylpropanoid metabolism in plants (Stracke et al. 2001;
377 Hartmann et al. 2005), initial searches were focused on identifying MYB-genes that were strongly expressed
378 in berries. However, members of the bHLH and bZIP families of transcription factors were also identified in
379 the berry RNA-seq databases since they may also play roles in regulating accumulation of phenolic
380 bioactives (Hartmann et al. 2005). These data could be particularly useful in breeding programs for selection
381 of plants with improved bioactive contents. The presence of genes encoding putative transporters of phenolic
382 compounds was also examined. Excretion of target compounds produced by bacterial hosts into the culture
383 medium via such transporters would simplify extraction procedures, and may also be necessary in case of
384 any toxic effects of these molecules in bacteria. One transporter is currently being characterized functionally.
385 Gram-positive bacteria as cell factories for the efficient production of berry high-value phenolics
386 While the search for novel bioactives was under way, a set of well-characterized biosynthetic pathway was
387 used for establishing a platform for production of phenolic compounds in Gram-positive bacteria. Two major
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388 criteria were used for the selection of host organisms for this task: (1) they had to be robust, well-studied
389 organisms with a long-standing history of industrial use and (2) the bacteria must have a generally regarded
390 as safe (GRAS) status and/or be recognized as food-grade. Two industrially-important bacteria, namely
391 Lactococcus lactis and Corynebacterium glutamicum, appeared as attractive candidates for the sustainable
392 production of added-value food and pharmaceutical compounds (Lahtinen et al. 2011; Jojima et al. 2013).
393 Both L. lactis and C. glutamicum are used worldwide in numerous industrial processes, which consequently
394 led to significant amount of research work dedicated towards improving our knowledge of their physiology
395 and genetics. These efforts have resulted in development of large toolboxes for (heterologous) gene
396 expression and introduction of genetic modifications in these bacteria (Burkovski 2008; Pontes et al. 2011;
397 Gaspar et al. 2013). Furthermore, high-throughput methodologies for transcriptome, proteome and
398 metabolome analysis, and genome-scale metabolic models are available for these species (Krömer et al.
399 2004; Oliveira et al. 2005; Shinfuku et al. 2009; Gaspar et al. 2013). Combined with their general robustness,
400 ease of handling and the GRAS status, both L. lactis and C. glutamicum have a strong potential for becoming
401 platform organisms for production of polyphenolic compounds.
402 It should also be noted that during the earlier stages of the project, E. coli was also as a platform for
403 reconstruction of polyphenol biosynthetic pathways. E. coli has a long history of use in research on
404 microbial-based production of various value-added chemicals. Moreover, it is one of the best studied
405 organisms, easy to handle, and there much larger set of genetic tools is available for this organism compared
406 to other bacteria (Yu et al. 2011; Dobson et al. 2012; Chen et al. 2013). Furthermore, E. coli has been
407 previously used for heterologous production of polyphenolic compounds (Pandey et al. 2016; Wang et al.
408 2016a).
409 Lactococcus lactis
410 L. lactis is a Gram-positive bacterium from the order of Lactobacillales that has been widely used in the food
411 industry for the manufacturing of cheese, buttermilk and other dairy products. It is a low G+C aerotolerant
412 bacterium with a relatively small genome and simple sugar metabolism: utilization occurs via glycolysis
413 (Embden–Meyerhof–Parnas pathway) with lactic acid being the major end-product (Gaspar et al. 2013). Its
17
414 ability to ferment various carbohydrates, such as hexoses, pentoses and, in some strains, even complex
415 carbohydrates of plant origin, as well as its high alcohol, acid and stress tolerance makes this bacterium
416 suitable for the harsh environments commonly found under industrial production conditions. L. lactis strains
417 have been engineered to produce multiple commodity chemicals (e.g. 2,3-butanediol and butanol), as well as
418 food additives, including sweeteners (e.g. mannitol and xylitol), flavorings (e.g. acetaldehyde and diacetyl),
419 and vitamins. Furthermore, production of plant secondary metabolites, namely isoprenoids and stilbenes, has
420 been demonstrated in this organism (Neves et al. 2005; Katz et al. 2011; Gaspar et al. 2013; Song et al.
421 2014). In addition, L. lactis is well known as an excellent host for the expression of both prokaryotic and
422 eukaryotic proteins, and has been consequently used for development of live vaccine (Kunji et al. 2005;
423 Hernández et al. 2007; Frelet-Barrand et al. 2010; Pontes et al. 2011).
424 As depicted in Figure 6, in plants the biosynthesis of the core intermediate of the phenylpropanoid pathway,
425 p-coumaric acid, begins from L-phenylalanine and occurs in two steps: a deamination reaction catalyzed by
426 phenylalanine ammonia-lyase (PAL) and a hydroxylation reaction catalyzed by cinnamate 4-hydroxylase
427 (C4H). The latter enzyme belongs to the CYP family that is often challenging to express in active form in
428 prokaryotic systems (Dvora and Koffas 2013). However, in some bacteria and fungi there exists an
429 alternative pathway where L-tyrosine is converted directly into p-coumaric acid via the action of tyrosine
430 ammonia-lyase (TAL), hence bypassing the C4H step (Figure 6). As a first step towards establishing a
431 platform strain for the production of polyphenols in L. lactis, in collaboration with Nielsen and coworkers,
432 we identified several novel TAL enzymes that specifically produce high levels of p-coumaric acid from L-
433 tyrosine (Jendresen et al. 2015).
434 To test the potential of L. lactis to produce more complex phenolic compounds, we introduced a pathway for
435 the biosynthesis of resveratrol. The introduced pathway consisted of three enzymes: TAL, 4CL (4-
436 coumaroyl-CoA ligase, which converts p-coumaric acid to p-coumaroyl-CoA), and STS (stilbene synthase,
437 which catalyzes the condensation of p-coumaroyl-CoA and three molecules of malonyl-CoA to yield trans-
438 resveratrol, Figure 6). The corresponding genes were placed on the high copy number vector pNZ8048
439 under the control of the nisin-inducible promoter PnisA. This promoter is a part of a tightly-controlled
18
440 inducible gene expression system which is based on the auto-regulatory mechanism controlling the
441 production of the bacteriocin nisin (Kuipers et al. 1993; Kuipers et al. 1998). The producer L. lactis strain
442 contains a chromosomally-integrated two-component system NisRK allowing induction of its target
443 promoters by addition of sub-inhibitory concentrations of the bacteriocin nisin to the exponentially-growing
444 cell culture (Linares et al. 2010). Upon cultivation of the producer strain in a chemically defined medium,
445 production of trans-resveratrol was detected in the order of several mg l-1. When TAL was replaced with a
446 promiscuous PAL/TAL enzyme (MacDonald and D’Cunha 2007), additional production of small amounts of
447 pinosylvin (trans-3,5-dihydroxystilbene) from L-phenylalanine was observed. Furthermore, production of
448 methylated variants of trans-resveratrol was also achieved (unpublished results). Obtained titers for trans-
449 resveratrol are comparable to that obtained in an early work on trans-resveratrol production in both E. coli
450 and yeast (Beekwilder et al. 2006; Choi et al. 2011). Current research efforts focus on improving the
451 production efficiency through metabolic engineering of the central carbon metabolism for increased
452 precursor supply.
453 Anthocyanins (glycosylated anthocyanidins) are another group of phenolic phytochemicals. In addition to
454 their antioxidant properties, anthocyanins are valuable for their colors, which range from orange to blue
455 (Falcone Ferreyra et al. 2012). They are of particular interest for the food industry as natural alternatives to
456 replace synthetic dyes. Anthocyanins are synthesized from flavanones by flavanone 3-hydroxylase (F3H),
457 which adds a hydroxyl group to the C3 position. Resulting dihydroflavonols are further processed by
458 dihydroflavonol 4-reductase (DFR), which catalyzes reduction of the carbonyl group of the C4 position to
459 form leucoanthocyanidins, which in turn are converted to anthocyanidins via the action of anthocyanidin
460 synthase (ANS). Anthocyanidins are further stabilized through a C3-glycosylation reaction usually catalyzed
461 by UDP-glucose:flavonoid 3-O-glucosyltransferase (UFGT) to form the anthocyanidin 3-O-glucosides
462 (Falcone Ferreyra et al. 2012) (Figure 6).
463 In order to reconstruct the anthocyanin production pathway in L. lactis, native genes and genes codon-
464 optimized for L. lactis from various plant species coding for F3H, DFR, ANS and UFGT were placed under
465 control of the nisin-inducible promoter PnisA on a high copy-number vector or were integrated into the
19
466 chromosome of L. lactis. Each protein was taggedg with an N- or C- terminal strep-tag, and Western blots
467 were performed to detect the heterologous proteins in cell-free extract of L. lactis. Activity of each enzyme
468 was probed in vitro and in vivo and the reaction products were analyzed using high performance liquid
469 chromatography (HPLC). Production of various types of cyanidin-derived anthocyanidins and anthocyanins
470 by the engineered L. lactis strains was detected, further demonstrating the potential of this lactic acid
471 bacterium as a production host for plant-derived bioactive compounds for food applications (unpublished
472 results). To our knowledge, this is the first report of using lactic acid bacteria for the production of complex
473 phenolic compounds.
474 Malonyl-CoA is a rate-limiting precursor in the biosynthesis of various phenolic compounds in bacteria (Xu
475 et al. 2011; Bhan et al. 2013). Under physiological conditions, L. lactis cells use malonyl-CoA as a substrate
476 in the chain elongation step of the fatty acid biosynthesis. FapR of Bacillus subtilis is a transcriptional
477 repressor that directly senses the intracellular pool of malonyl-CoA. A metabolic biosensor based on the
478 interaction of malonyl-CoA with FapR was previously designed in Escherichia coli (Xu et al. 2013). As a
479 part of the efforts to enhance polyphenol production, a transcriptional biosensor for malonyl-CoA was
480 constructed for the use in C. glutamicum but also proved to be fully functional in L. lactis and could
481 therefore be used for screening of variants with an enhanced malonyl-CoA pool.
482 Among other activities that were pursued in the early stages of the project, we also tried to express plant
483 CYPs in L. lactis, as many steps of the polyphenol biosynthetic pathway are catalyzed by these heme-bound
484 monooxygenases. Functional expression of genes encoding membrane-anchored CYPs in prokaryotes is
485 known to be notoriously difficult, and is limited by problems associated with the differences in intracellular
486 localization (natively to the plant endoplasmatic reticulum), membrane composition, protein folding, and
487 cofactor utilization. The latter element became especially crucial when attempting to express these proteins
488 in L. lactis, because this, normally anaerobic, bacterium does not have the ability to synthetize heme, despite
489 undergoing a metabolic shift to respiration when supplemented with an exogenous source of heme and being
490 capable of regulating heme homeostasis (Pedersen et al. 2012).
20
491 We focused our efforts on CYP79A1 from Sorghum bicolor as it is a well-characterized CYP involved in
492 dhurrin biosynthesis (Bak et al. 2000), and has been previously successfully produced in E. coli (Vazquez-
493 Albacete et al. 2016). As a testbed for the expression of this CYP we have used the Waldo platform (Waldo
494 et al. 1999) that was previously instrumental in producing functional CYP79A1 in E. coli. Briefly, the
495 platform is based on a fusion of the membrane protein with a GFP folding reporter at the C terminus: the
496 fused GFP is designed to act as a fluorescent reporter only when the membrane protein is properly folded
497 and its C terminus is oriented towards the cytoplasm.
498 We detected CYP79A1-GFP by both fluorescence measurements of the L. lactis culture and in-fluorescence
499 protein gels, but were not able to detect activity of the CYP in our in vitro tests. Purified membrane protein
500 fractions from cell cultures grown both aerobically (with hemin supplementation) and anaerobically (with
501 addition of hemin after purification of membrane proteins) did not show any product formation in our TLC-
502 based assay (Blomstedt et al. 2012). These results suggested that CYP79A1 was probably misfolded in L.
503 lactis, although we did not perform any follow-up experiments to confirm this hypothesis. The topic was not
504 pursued any further because any CYP active in the biosynthetic pathways for the selected model compounds
505 could be by-passed (for example the C4H-catalyzed step could be by-passed by using TAL instead of PAL).
506 Corynebacterium glutamicum
507 C. glutamicum is an important organism in industrial biotechnology for the microbial production of bulk
508 chemicals, in particular of amino acids (Becker and Wittmann 2012). C. glutamicum is a very robust
509 microorganism, which shows high resistance to the presence of small aromatic compounds (Liu et al. 2013),
510 which renders C. glutamicum a very promising host for the production of pharmacologically interesting
511 plant-derived polyphenols (Marienhagen and Bott 2013). Until recently, the activity of a complex catabolic
512 network for aromatic compounds meant that C. glutamicum was not used for the production of aromatic
513 compounds (except for aromatic amino acids) (Shen et al. 2012) due to rapid degradation of the products of
514 interest. However, C. glutamicum can be easily modified genetically and a toolbox for the high-level
515 expression of heterologous genes originating from other organisms, has become available, and is based on a
21
516 set of strong and inducible promoters (Eggeling and Bott 2005; Pátek and Nešvera 2013; Kortmann et al.
517 2015).
518 Surprisingly, in initial experiments, it turned out that C. glutamicum is able to grow on naturally occurring
519 phenylpropanoids such as p-coumaric acid, ferulic acid, caffeic acid and 3-(4-hydroxyphenyl)-propionic acid
520 as sole carbon and energy sources. The maximum growth rates on the selected phenylpropanoids ranged
521 from 0.15 to 0.23 h-1 corresponding to doubling times of 3 to 4.5 hours (Kallscheuer et al. 2016a). We
522 suspected that the activity of this pathway would come into conflict with any attempt to produce polyphenols
523 as both pathways compete for phenylpropanoids. Unfortunately, the underlying metabolic pathway
524 responsible for the degradation of phenylpropanoids in C. glutamicum was unknown at the start of the
525 BacHBerry project. Global gene expression analyses were conducted, which led to the identification of a
526 gene cluster consisting of seven genes (designated phdT, phdA, phdR, phdB, phdC, phdD and phdE). In
527 response to feeding of the above-mentioned phenylpropanoids these genes were upregulated up to 100-fold
528 (Kallscheuer et al. 2016a). In subsequent studies, it turned out that the genes in this cluster code for the
529 enzymes of the phenylpropanoid catabolic pathway in C. glutamicum. The gene phdT codes for a
530 phenylpropanoid transporter protein, whereas phdA and the predicted operon phdBCDE code for enzymes
531 involved in the degradation of phenylpropanoids. The phd gene cluster is transcriptionally controlled by a
532 MarR-type repressor encoded by phdR. Cultivation experiments conducted with C. glutamicum strains
533 carrying single-gene deletions showed that loss of phdA, phdB, phdC or phdE abolished growth of C.
534 glutamicum with all tested phenylpropanoid substrates. The intracellular accumulation of pathway
535 intermediates determined via LC-ESI-MS/MS in single-gene deletion mutants showed that the phd gene
536 cluster encodes for enzymes involved in a CoA-dependent, β-oxidative deacetylation pathway, which is
537 essential for the utilization of phenylpropanoids in C. glutamicum. The identified pathway thereby represents
538 a peripheral catabolic route responsible for chain shortening of phenylpropanoids to benzoic acids and
539 acetyl-CoA (Figure 7). C. glutamicum converts the resulting intermediates benzoate, 4-hydroxybenzoate and
540 protocatechuate to succinyl-CoA and acetyl-CoA by central degradation pathways for aromatic compounds
541 (especially the well-characterized β-ketoadipate pathway), and catabolites are ultimately channeled into the
22
542 tricarboxylic acid cycle (Shen and Liu 2005) (Figure 7). Unexpectedly, C. glutamicum failed to grow on the
543 phenylpropanoid, cinnamic acid, which lacks ring para-hydroxylation. Astonishingly, a C. glutamicum strain
544 with a single deletion of the gene phdR coding for the identified transcriptional repressor was able to grow
545 with cinnamic acid as sole carbon and energy source (Kallscheuer et al. 2016a). This indicated that PhdR has
546 a pronounced specificity for hydroxylated phenylpropanoids, especially when hydroxylated in para-position
547 of the aromatic ring. In the course of our studies, cinnamic acid was the only phenylpropanoid tested lacking
548 this hydroxylation, so the complete specificity of this catabolic pathway of phenolics in C. glutamicum
549 remains unknown.
550 During construction of the C. glutamicum platform strain for plant polyphenol production, four gene clusters,
551 comprising 21 genes, including four genes of the phd cluster involved in the catabolism of aromatic
552 compounds, were deleted. The resulting platform strain C. glutamicum DelAro4 was unable to degrade any
553 phenylpropanoid tested, and also could not utilize simple benzoic acids such as protocatechuate (Kallscheuer
554 et al. 2016c). This strain was used as a chassis for the microbial production of polyphenols in particular,
555 stilbenes and (2S)-flavanones, in C. glutamicum.
556 Plasmid-borne expression of codon-optimized genes coding for a 4CL from parsley (Petroselinum crispum),
557 a chalcone synthase (CHS) and a chalcone isomerase (CHI) from Petunia x hybrida from the strong T7
558 promoter in this strain background enabled production of (2S)-flavanones (Figure 7). Maximal titers of 35
559 mg l-1 naringenin and 37 mg l-1 eriodictyol were obtained from the supplemented phenylpropanoids p-
560 coumaric acid and caffeic acid, respectively. Expression of the 4cl gene in combination with a codon-
561 optimized gene coding for an STS from peanut (Arachis hypogaea) allowed for the production of the
562 stilbenes pinosylvin, trans-resveratrol and piceatannol starting from supplemented phenylpropanoids
563 cinnamic acid, p-coumaric acid and caffeic acid, respectively (Kallscheuer et al. 2016c) (Figure 7). Stilbene
564 concentrations of up to 158 mg l-1 could be achieved in defined CGXII medium with 4% glucose and
565 supplemented with cerulenin, a fatty-acid biosynthesis inhibitor that is known to enhance polyphenol
566 production in bacteria by increasing the intracellular concentrations of malonyl-CoA (Santos et al. 2011; van
567 Summeren-Wesenhagen and Marienhagen 2015; Cao et al. 2016). Engineering amino acid metabolism for an
23
568 optimal connection to the synthetic plant polyphenol pathways was conducted to enable stilbene production
569 directly from glucose. Indeed, the additional heterologous expression of aroH from E. coli (coding for the
570 first enzyme of the shikimate pathway responsible for the synthesis of aromatic amino acids) together with a
571 codon-optimized gene coding for a TAL from Flavobacterium johnsoniae led to the production of up to 60
572 mg l-1 trans-resveratrol in the absence of supplemented p-coumaric acid (Kallscheuer et al. 2016c).
573 Furthermore, C. glutamicum was used to produce trans-resveratrol from a cheaper precursor 4-
574 hydroxybenzoic acid (HBA) through reversal of a microbial β-oxidative phenylpropanoid degradation
575 pathway (Kallscheuer et al. 2016b). This novel synthetic pathway circumvents any need for the endogenous
576 supply of aromatic amino acids as polyphenol precursors and in C. glutamicum instantly yielded 5 mg l-1
577 trans-resveratrol from supplemented HBA without any further optimization. Very recently, we also
578 engineered C. glutamicum to produce more complex polyphenols including flavonols (kaempferol and
579 quercetin) and O-methylated stilbenes (pinostilbene and pterostilbene). The obtained pterostilbene titer was
580 42 mg l-1, which is comparable to the titer of 50 mg l-1obtained in engineered E. coli. In C. glutamicum, for
581 the flavonols kaempferol and quercetin titers of 23 mg l-1 and 10 mg l-1, respectively, were detected. The
582 aforementioned titers were obtained from the phenylpropanoids p-coumaric acid and caffeic acid ,
583 respectively, as precursors and represent the highest flavonol titers obtained in engineered microorganism
584 until today (Kallscheuer et al. 2017).
585 The main bottlenecks for the production of polyphenols turned out to be the TAL activity as well as the
586 intracellular availability of malonyl-CoA, representing the cosubstrate in the STS- and CHS-reactions. On-
587 going efforts to improve polyphenol production in C. glutamicum therefore focus mainly on improving the
588 activity of the rate-limiting enzymes and increasing the intracellular malonyl-CoA pool.
589 Other activities
590 As a part of this work, we also aimed at reconstruction of a previously unknown biosynthetic pathway for the
591 flavonol fisetin (3,7,3′,4′-tetrahydroxyflavone, Figure 8). Fisetin is a potent anti-inflammatory, anti-cancer,
592 and anti-oxidant compound (Suh et al. 2008; Funakoshi-Tago et al. 2011; Jash and Mondal 2014). Moreover,
24
593 it has been demonstrated to have a neuroprotective effect and also to limit the complications of type I
594 diabetes (Maher 2009; Maher et al. 2011). Based on the molecular structure of this compound, we
595 hypothesized that its biosynthesis should follow the general pathway of flavonol biosynthesis (Figures 6 and
596 8): a flavanone precursor would be converted into dihydroflavonol by F3H, and then further converted into
597 the corresponding flavonol via the action of flavonol synthase (FLS). In the last step of the biosynthesis, a
598 hydroxyl group would be added at the C3’ position by flavonoid 3’-hydroxylase (F3’H). This part of the
599 work was carried out in E. coli. Isoliquiritigenin (2',4',4-trihydroxychalcone) appeared to be the most suitable
600 precursor, and when supplied to an E. coli culture expressing F3H and FLS from Arabidopsis thaliana,
601 production of garbanzol and resokaempferol was detected. Resokaempferol could be further converted into
602 fisetin by expression of F3’H genes from A. thaliana and Pn. hybrida fused with a cytochrome-P450
603 reductase (CPR) from Catharanthus roseus (Stahlhut et al. 2015). The three enzymes were then combined
604 with the isoliquiritigenin biosynthetic pathway, consisting of TAL from Rhodobacter sphaeroides, 4CL from
605 Pr. crispum, CHI from Medicago sativa, and a fusion protein of CHS from Pn. hybrida and CHR from
606 Astragalus mongholicus, to yield 0.3 mg l-1 of fisetin (Stahlhut et al. 2015). This work allowed reconstruction
607 of a complete pathway for the biosynthesis of fisetin from L-tyrosine as the starting precursor using enzymes
608 from various sources, and the identified pathway is currently being transferred to Gram-positive hosts.
609 Bioinformatics and modelling
610 There were multiple activities within BacHBerry, both within the discovery and the microorganism
611 engineering sectors, which required the use of bioinformatics tools. Hence, the mathematical modeling and
612 computational parts of the project aimed at addressing three major problems. The first two were related to
613 gene expression, either for performing functional genomics analysis using the RNA-seq data from berries or
614 to design sequences for heterologous expression of plant genes in microbes. The third problem concerned
615 metabolic network optimization for both a single population and for a community of microbes. We detail
616 below the main results obtained on each of the above activities. Other subjects that were addressed as a part
617 of this project were metabolic communication between the different species in a community (Milreu et al.
618 2014; Andrade et al. 2016) and modeling dynamic metabolic networks for potential application in strain
25
619 optimization (Costa et al. 2014; Hartmann et al. 2015a; Hartmann et al. 2015b; Costa et al. 2016; Hartmann
620 et al. 2016).
621 Sequencing data analysis and plant functional genomics
622 Understanding which genes are differentially expressed during phenolic production by plants requires the
623 capacity to undertake a rapid analysis of RNA-seq data. One of the main problems to be addressed in this
624 case is the search for coding sequences that are homologous to those already present in sequence databases,
625 which involves identification, selection and annotation of the best matches. Currently, such analyses rely
626 heavily on manual curation as well as on the manual assembly of results obtained using different
627 computational procedures and sequence databases. Automated processes become crucial in the light of the
628 ever-increasing amounts of generated transcriptome data. Furthermore, automation of processes also allows
629 improvement of the quality of the analyses, as the likelihood of human error grows with the amount of
630 manual processing and verifications necessary. Moreover, most of the existing tools are able to annotate the
631 transcriptome but lack the curation functionalities that are as important as the primary homology-based
632 identification of candidate genes.
633 As mentioned previously we developed a pipeline called MassBlast (Veríssimo et al. submitted), to improve
634 and accelerate the annotation procedure of RNA-seq data. Besides allowing for operators to query multiple
635 databases for performing homology searches, the tool also provides a set of parameters that can be defined in
636 a user-friendly way. MassBlast seeks the best alignment between the sequences in a dataset consisting of
637 contigs obtained from RNA-seq assemblies and a database of known sequences. In the context of BacHBerry
638 and as a general validation of the method, MassBlast was used for the analysis of the blackberry
639 transcriptome, and preliminary results showed a significant improvement in annotation speed (five minutes
640 versus at least 26 working days, to annotate all known CYP families in blackberry) with very high
641 accuracies. MassBlast is freely available at https://github.com/averissimo/mass-blast/.
642 Sequence design for heterologous expression
26
643 Synthetic biology aims at constructing microorganisms capable of producing economically-relevant
644 chemical products in a cost-effective way, which involves optimizing the functioning of metabolic pathways
645 and reactions. These pathways and reactions involve proteins that are frequently heterologous. In order to
646 increase the production of metabolites, it is possible to optimize heterologous protein production in the target
647 host. This may be done by altering the codons in the gene that encodes for that protein. Each codon is a
648 sequence of three nucleotides encoding one amino acid that is part of a protein. Since each amino acid can be
649 encoded by several synonymous codons, a given protein can be expressed using a large number of different
650 codon combinations.
651 To address this problem, we worked on improving the machine learning models capable of predicting protein
652 expression levels based on their codon usage frequency (Fernandes et al. 2016) by using Support Vector
653 Regression (SVR) and Partial Least Squares (PLS). SVR yields predictions that surpass those of PLS. It has
654 been shown that it is possible to improve the predictive ability of the models by using two additional input
655 features, namely codon identification number and codon count, besides the already used codon bias and
656 minimum free energy. In addition, applying ensemble averaging to the SVR or the PLS model improved the
657 results even further. Different ensembles and features can be tested with the aim of further improving the
658 prediction models.
659 Metabolic network optimization
660 Another goal of the computational activities of BacHBerry was to explore methods for in silico metabolic
661 engineering of phenolic production in L. lactis and C. glutamicum. For this we selected four model target
662 compounds: fisetin, trans-resveratrol, pelargonidin and quercetin. Additionally, optimization of malonyl-
663 CoA production was also considered, since this is known to be a major bottleneck in heterologous flavonoid
664 production. The first step involved performing network optimization for a single population, which included
665 prediction of knock-out strategies and determination of minimal sets of genetic interventions that could
666 guarantee an increased production of the target compound production based on stoichiometric models for the
667 two organisms. These predictions were compared with experimental results and model refinement was
27
668 conducted in cases of discrepancies. For the second step, we considered a use of microbial consortia for
669 production purposes. A method was developed that is being used for modeling the production of the
670 compounds of interest for BacHBerry using L. lactis and C. glutamicum. Other, possibly larger consortia,
671 may be considered in the future.
672 Single strain-based production
673 Recently, several computational methods for in silico metabolic network optimization have emerged and are
674 currently being applied in metabolic engineering (Vera et al. 2010; Zomorrodi et al. 2012; Machado et al.
675 2015; Simeonidis and Price 2015; Bergdahl et al. 2015). They are based on different approaches and
676 rationales, thereby leading to distinct solutions. One key issue is the multitude of results, known to be
677 dependent on the specific algorithms and solvers, used. In order to address this problem, a consensus-based
678 approach was developed, following our work on meta-analysis of transcriptomic studies (Caldas et al. 2014).
679 This method is based on running several optimization procedures and analyzing a posteriori the solutions,
680 looking for a consensus. The rationale is to have rankings of hypotheses that may provide confidence in
681 particular sets of proposed genetic alterations from various aspects.
682 In the case of a single population of microbes (i.e. a monoculture), the genome-scale models of L. lactis and
683 C. glutamicum were extended with the biosynthetic pathways of the four above-mentioned target compounds
684 (Figure 9). Subsequently, five different optimization methods were applied to the genome-scale models
685 (extended with the heterologous pathways): OptKnock (Burgard et al. 2003), MOMA (Segrè et al. 2002),
686 OptGene (Patil et al. 2005), RoboKod (Stanford et al. 2015), and RobustKnock (Tepper and Shlomi 2010),
687 and the consensus ranking was obtained for each strain and compound. The hypotheses were generated with
688 the rank product test and the outputs were lists of gene deletions that would achieve the best ranks using
689 defined criteria, namely maximum predicted target compound production, maximizing the minimum
690 predicted target compound production and distance from the wild-type flux distribution. These results were
691 then further filtered and experimentally validated, resulting in several promising mutant strains where
28
692 production was increased by up to three-fold. Implementation of this pipeline is possible via a software
693 package called OptPipe, available at https://github.com/AndrasHartmann/OptPipe.
694 We then explored the concept of multi-objective optimization in the field of metabolic engineering when
695 both continuous and integer decision variables were involved in the model. In particular, we proposed multi-
696 objective models to suggest reaction deletion strategies and to deal with problems where several functions
697 must be optimized simultaneously, such as maximization of bioproducts while minimizing toxicity. We thus
698 introduced Multi-Objective Mixed integer Optimization for metabolic engineering, a computational
699 framework that aims to model and solve optimization problems, generated for predicting reaction knockout
700 strategies by means of multi-objective programming. We compared the results from our method with those
701 obtained by using the well-known bi-level optimization model (OptKnock). Furthermore, we studied two
702 multi-objective optimization problems arising from the metabolic engineering of microorganisms, showing
703 that indeed a multi-objective setting is promising, as it expands the set of generated hypotheses and allows
704 inclusion of multiple goals, simultaneously.
705 For a community of microbes
706 Pure microbial cultures have long been used for production of high-value compounds, as illustrated by the
707 example of S. cerevisiae producing artemisinic acid, an important precursor for the anti-malaria drug
708 artemisinin, on a large scale (Ro et al. 2006; Lenihan et al. 2008). However, there is increasing evidence that
709 such processes could run more efficiently if a community of microbial species is used (Logan and Rabaey
710 2012; Jones et al. 2016; Wen et al. 2016). There are multiple reasons including an increased production
711 efficiency of the system due to lessening of metabolic burden for individual cells. Another important
712 consideration is that such community systems might have higher chances of avoiding toxic effects specific
713 for individual microorganisms. Microbial consortia are believed to be able to sustain more complex
714 production pathways, as well as being more robust as a group. The challenge, however, remains in
715 establishing precisely which set of strains or species is best for the production of any given compound.
29
716 As part of BacHBerry, we introduced an initial topological model and a combinatorial algorithm that enabled
717 us to propose optimal consortia to produce compounds that were either exogenous to the consortium, or were
718 endogenous but where interaction between the species in the consortium could improve the production line
719 (Julien-Laferrière et al. 2016). For initial validations of the model and the method, we applied it to two case-
720 studies taken from the literature. The first case involved production of two bioactive compounds, penicillin
721 and cephalosporin C, both of which are antibiotics used in the pharma industry. A synthetic consortium
722 composed of four different species, three Actinobacteria (Streptomyces cattleya, Rhodococcus jostii RAH_1
723 and Rc. erythropolis BG43) and one methanogenic Archaea (Methanosarcina barkeri), was then run through
724 the algorithm, allowing introduction of genetic manipulations and regulatory processes. We showed that the
725 best solution involved only two of the four species, namely St. cattleya and M. barkeri (Julien-Laferrière et
726 al. 2016).Another considered case was a consortium that may be seen as artificial in the sense that it
727 associates two wild-type populations of microbes, pathogenic γ-proteobacteria Klebsiella pneumoniae and
728 methanogenic Archaea M. mazei, which normally do not interact in nature. K. pneumoniae is a natural
729 producer of 1,3-propanediol, which is an important building block for polymer production. Our results
730 demonstrated that co-cultivation of K. pneumoniae with M. mazei may enable obtaining higher yields of this
731 compound in Klebsiella (Julien-Laferrière et al. 2016). This supported a previous study involving co-
732 cultures of Clostridium sp. with methanogenes such as Methanosarcina sp. CHTI55 (Koesnandar et al.
733 1990). The developed algorithms are currently being used for developing consortia of polyphenol-producing
734 Gram-positive bacteria. This task was inspired by the work of Jones and colleagues, who were able to
735 achieve an overall 970-fold improvement in titers for flavan-3-ols production from phenylpropanoic acids by
736 using a co-culture of two E. coli strains with further optimization of cultivation conditions, as compared to
737 previous studies that used monocoltures for this purpose (Jones et al. 2016).
738 Recovery and purification of polyphenols from fermentation broth
739 Downstream processing is an important part of any production process and consists of steps required for
740 recovery and purification of biosynthetic products from fermentation broth, which may have a significant
741 impact on the final costs of a product (Goldberg 1997). In the design of downstream processing within
30
742 BacHBerry, trans-resveratrol (Figure 7) was used as the test product, being one of the model phenolic
743 compounds that was successfully produced in both C. glutamicum and L. lactis.
744 In a typical fermentation broth, most of the components present are either hydrophilic (e.g., salts, organic
745 acids, non-consumed carbon sources (usually glucose), nitrogen source (ammonium sulfate, for instance)) or
746 are much larger molecules than trans-resveratrol (e.g., proteins, nucleic acids, cells in suspension). This
747 suggested a downstream processing design strategy that used either solubility and hydrophobicity as the
748 driving forces, or alternatively – size exclusion. Since, at low concentrations (around 100 mg l-1),
749 crystallization would involve high energy costs per kg of product recovered, this option was discarded. Two
750 possible extraction techniques, namely liquid-liquid extraction and solid-liquid adsorption, were investigated
751 as means of recovering and purifying extracellularly produced trans-resveratrol from a C. glutamicum
752 fermentation broth containing spent CGXII medium and glucose, due to the higher production efficiency
753 found in this bacterium. The downstream process design aimed at a recovery of at least 80% and a purity of
754 95% trans-resveratrol.
755 Liquid-liquid extraction is a technique to separate a given compound from a feed solution using an
756 immiscible auxiliary phase that should be selective for that compound (Cox and Rydberg 2004). The
757 logarithm (base 10) of the partition coefficient of trans-resveratrol between the aqueous and different
758 potential organic phases was determined experimentally for several different solvents (Figure 10). The spent
759 fermentation broth from C. glutamicum was used as the aqueous phase in these experiments.
760 All tested organic solvents performed very well in extracting trans-resveratrol (minimum log P value is ~0.5,
761 corresponding to a partition coefficient of 3.2), identifying them as suitable candidates for the recovery of
762 this compound (when having different impurities present) (Figure 10).
763 One of the drawbacks of using liquid-liquid extraction, however, is that it may interfere with protein folding
764 and stability (Prince and Smith 2004). Besides measuring the partition of the polyphenols of interest in the
765 aqueous and organic phases, the same analysis was performed for the total protein content. Although a
766 considerable part of the total protein remained in the aqueous phase, some of the protein seemed to be
31
767 present in between the two liquid phases (probably as a result of denaturation and foaming) (Figure 11).
768 This phenomenon might reduce the recovery of the desired phenolics, as it was also observed experimentally
769 (data not shown).
770 Solid-liquid adsorption using hydrophobic resins is another way of purifying trans-resveratrol. As in liquid-
771 liquid extraction, the high hydrophobicity of trans-resveratrol can be used to recover it selectively from the
772 fermentation broth, leaving the more hydrophilic impurities behind. After an initial resin screen, one of the
773 resins selected was the polystyrene-divinylbenzene based Amberlite XAD-16 from Sigma-Aldrich (particle
774 diameter: 250-841 µm; dipole moment: 0.3; surface area: 900 m2 g-1; pore diameter: 100 Å).
775 To test the ability of this resin to recover trans-resveratrol from C. glutamicum fermentation broth
776 (containing CGXII spent medium and glucose), a lab-scale experiment was performed with an AKTA
777 Explorer chromatographic workstation (GE Healthcare, Sweden) (see Figure 12). A 30 ml column was
778 packed with resin and pre-equilibrated with MilliQ water. Afterwards, 10 ml of sample was injected and a
779 washing step with pure water was performed for 10 column volumes (CV). During elution, a gradient of
780 water/ethanol was run for about 12 CV and the peak of trans-resveratrol showed up at around 18 CV, at an
781 ethanol percentage of 50% (v/v). Regeneration was performed with 100% ethanol.
782 More hydrophilic molecules such as organic acids, sugars and salts appeared in the flow-through (the first 10
783 CV). During the water/ethanol gradient, molecules desorbed as a function of their hydrophobicity. More
784 hydrophobic molecules, such as trans-resveratrol, are supposed to elute at higher ethanol content in the
785 mobile phase. In this case, trans-resveratrol appeared to be the compound eluting between 16 and 20 CV,
786 since it also showed absorption at 304 nm (Camont et al. 2009).
787 Although both liquid-liquid extraction and solid-liquid adsorption present their own advantages and
788 disadvantages, a strong point for using adsorption is the use of ethanol instead of any other organic solvent
789 that might not be regarded as GRAS. Moreover, not using liquid-liquid extraction avoids any possible
790 problems related with protein precipitation and emulsification. The ethanol used during elution in adsorption
32
791 needs to be recovered from water by distillation (the azeotrope poses no problem as 80% (v/v) ethanol can be
792 used).
793 The proposed downstream process started with a solid-liquid separation step by centrifugation to separate
794 biomass from the liquid broth. The clarified liquid phase was then sent continuously to one of two adsorption
795 columns, operating staggered in time. The fraction rich in trans-resveratrol was subsequently evaporated,
796 resulting in a wet slurry containing the polyphenol in the solid phase. This slurry was filtered in a rotary
797 vacuum filter and washed with water to remove remaining impurities adhered to the surface of the crystals.
798 The final step was a drying step to obtain the product in its final solid form. All the outlet streams containing
799 ethanol were directed to a distillation column in order to recover the solvent. A make-up stream ethanol was
800 added to compensate for losses.
801 Optimization of phenolic production by fermentation and scale-up of bioprocesses
802 There are multiple differences between microbial cultivation in shake flasks versus in bioreactors. The
803 former lacks monitoring and control of various parameters, such as dissolved O2 concentration and pH (Zelle
804 et al. 2010). Furthermore, the fed-batch fermentation mode, where one or more nutrient is being supplied
805 throughout cultivation (Moulton 2014), can be done in a controllable and reproducible fashion only in
806 bioreactors. These properties make bioreactors a suitable stage for optimization of cultivation conditions,
807 often leading to significant improvements in production yields (Hujanen et al. 2001; Ratnam et al. 2003; Li
808 et al. 2010; Rani and Appaiah 2011).
809 From the beginning of the project it was clear that optimization of fermenter operation would need to be
810 carried out using a model compound, because any newly-identified compounds would only be available at
811 the end of the project. Hence this section of the project was focused on optimization of production of the
812 model compound trans-resveratrol. However, it was expected that the strategies defined and the conclusions
813 extracted from these results on trans-resveratrol will be valid for the optimization of the fermentation
814 processes for the production of other target compound. Several L. lactis and C. glutamicum strains designed
815 within the project were evaluated and used for fermenter process optimization. The experiments were carried
33
816 out in agitated 2 to 5 litre fermenters with automatically controlled pH, dissolved oxygen and temperature.
817 Whenever required, oxygen was transferred to the medium by air injection at the bottom of the vessel.
818 A major goal of the project was the development of an economically feasible and sustainable industrial
819 process, therefore the following key optimization criteria were defined: (a) minimisation of the operation and
820 investment cost and (b) maximisation of the production rate and operation yield. To achieve these
821 optimization goals, the following variables have been investigated: (i) fermentation medium composition, (ii)
822 dissolved oxygen concentration, (iii) operation strategy (batch vs. fed-batch and integrated vs. separate
823 downstream operation). Our experiments suggested that there were several constraints limiting the operation
824 strategy to be implemented, namely: cell metabolism, the stability of trans-resveratrol inside the fermenter,
825 and the affinity of trans-resveratrol for the adsorption medium and solvents compared to other compounds
826 present in the fermentation broth.
827 Medium composition is an important parameter that often has a significant contribution towards the final
828 production costs. According to our initial experiments, supplementation of C. glutamicum minimal medium
829 with Brain-Heart Infusion (BHI) and a fatty-acid biosynthesis inhibitor, cerulenin, had a strong positive
830 effect on the final titers of trans-resveratrol. However, removing these two components from the medium
831 reduced medium costs by approximately 300-fold, resulting in a more than 50-fold drop in production costs
832 (267 €/g compared to 5 €/g of trans-resveratrol without supplementation of BHI and cerulenin).
833 We also evaluated the feasibility of producing phenolic compounds from waste materials, namely cheese
834 whey using L. lactis as a host. Whey is a lactose-rich dairy industry by-product with a high environmental
835 impact, if dumped untreated (Palmieri et al. 2017). To achieve this task, our production strain was modified
836 to allow it to grow and produce trans-resveratrol from lactose by introducing a plasmid carrying the lac
837 operon (Maeda and Gasson 1986). Production at 2 litre-scale was tested using lactose as carbon source and
838 comparing the productivity between the cultures with glucose or lactose. These experiments demonstrated
839 that the production of trans-resveratrol from lactose, and therefore from whey, is possible with slightly
840 inferior productivity compared to glucose. However, if whey is chosen as the fermentation medium, other,
34
841 more important, issues arise. The cost of whey is comparable to the cost of glucose but the cost of the
842 fermentation medium is only a small part of the total production costs, so variables such as whey availability
843 and quality, as well as the stability of supply and collecting logistics will determine the feasibility of the
844 operation more than the small losses in productivity. Clearly, if a phenolic production industrial unit were to
845 be implemented inside a dairy company with excess whey, the whey might then be the best raw material.
846 Another parameter investigated was the influence of dissolved oxygen concentration on the production
847 efficiency of L. lactis. This bacterium can activate its respiratory chain in the presence of hemin in the
848 culture medium, and this has been demonstrated to enhance its survival allowing it to grow to higher cell
849 densities (Duwat et al. 2001). Therefore the production strain efficiency was tested under the following
850 conditions: aerobic with hemin added to the fermentation broth, aerobic without hemin, semi-aerobic, and
851 fully anaerobic. In line with the available data, there was a significant increase in the final biomass when the
852 strain was grown under aerobic conditions with hemin. However, the opposite was observed for the
853 production of trans-resveratrol where the highest amounts of the compound were detected under anaerobic
854 conditions.
855 Lastly, we have also worked on establishing a system for continuous product removal from the fermentation
856 broth in order to simplify downstream processing and reduce trans-resveratrol oxidation due to prolonged
857 incubation. Two strategies for continuous adsorption and storage of secreted trans-resveratrol in a resin have
858 been devised and tested: (i) “in situ” product removal that consisted of adding a resin directly to the
859 fermentation broth and (ii) “continuous product removal” (ConPR) that involved constant pumping of the
860 fermentation broth through a column filled with the adsorption resin. The supernatant was separated from
861 cells via a tangential flow micro-filtration device, sent though the column, and then returned back into the
862 fermenter (Figure 13).
863 The obtained results demonstrated that both the “in situ” and ConPR strategies lowered the trans-resveratrol
864 productivity, probably due to co-adsorption of p-coumaric acid, the precursor for trans-resveratrol. This
865 effect was most pronounced in the “in situ” operation mode due to continuous contact of the resin with the
35
866 supernatant and cells. In the ConPR operation mode the effect was reduced due to low supernatant flow rate
867 through the resin, however adsorption of trans-resveratrol was also reduced. In contrast, in the “in situ”
868 mode the amount of adsorbed end-product was nearly identical to the amount of extracellular and
869 intracellular trans-resveratrol produced in control experiments without addition of resin. Although the two
870 techniques did not increase the overall productivity, they still simplified the downstream processing and may
871 lead to a production cost reduction by eliminating the need to break down the cells. However, future research
872 may provide a solution to avoid precursor co-adsorption resulting in increased polyphenol productivity.
873 Dissemination, training, societal and ethical issues
874 In addition to applying the latest technological advances from life sciences for establishing a pipeline for
875 microbial-based polyphenol production, BacHBerry analyzed the economic potential of the possible products
876 developed from this research. The assessment of economic issues of the project has been focused on the
877 market potential of the natural colorants identified during the screening of the germplasm collection. This
878 was done along with obtaining an overview of the relevant regulations and procedures for approval of novel
879 food additives, including colorants, in the EU, the United States (US), and China. Regulations within both
880 the EU and China confer a strict set of rules for bringing a novel food colorant, even a natural one, to the
881 market. These rules consider not only the composition and purity of the substance, but also the process
882 through which the compounds have been produced. In contrast, in the US, a new food additive can gain
883 access to the market by simply passing the GRAS assessment. Hence, if a novel polyphenolic colorant could
884 be produced using engineered microbial cell factories, the US market should probably be the first market to
885 be targeted, owing to less strict regulatory requirements for the approval.
886 Another topic addressed in BacHBerry was access and benefit sharing of genetic resources. As the project
887 was built upon screening of berry species from multiple countries, including China, Russia, Chile, Portugal,
888 and the UK, it is important to harness these resources in a responsible manner. The Nagoya Protocol on
889 Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization
890 (NP-ABS) is an agreement consisting of a set of rules for fair sharing of monetary and non-monetary benefits
36
891 arising from the usage and commercialization of genetic resources and traditional knowledge associated with
892 those. It aims at achieving fairness and equity of gain distribution, especially towards indigenous and local
893 communities (SCBD 2011; Morgera et al. 2014). The Protocol came into force in the EU as of November
894 20th 2016. As most of the BacHBerry project partners were from EU Member States, we began collecting
895 information about the best practices suggested from both the Convention on Biological Diversity (CBD) and
896 the EU, and kept monitoring the relevant guidelines and updates from the EU on implementing ABS. Among
897 the home countries of the project partners China, Denmark, France, Germany, Switzerland, and UK ratified
898 or accepted the NP-ABS (as of November 2016) and began implementing the aforementioned regulations
899 (European Parlament 2014). The UK also made its own regulation on implementing the Protocol (European
900 Parlament 2014; NMRO (UK) 2015; Secretary of State (UK) 2015).
901 Funded under the theme of “Knowledge-based Bio-Economy”, a final aim of the project was to provide
902 scientific and technical solutions to harness berry resources, as well as to contribute to sustainable
903 development of the economy. We consequently investigated the possible contributions of our research to
904 build a more sustainable society based on environmental, economical, and social impacts (Pei and Schmidt
905 2016). Furthermore, we conducted several open dialog events, namely workshops and interviews with
906 potential stakeholders from Austria, Denmark, and China. One of the surprising outcomes was that the
907 general public raised concerns over the use of genetically-modified organisms (GMOs) in the pipeline a lot
908 less frequently than originally expected. A free, science-based game for mobile devices called “Berry
909 Maker” was produced (http://www.berrymaker.com). The game allows the user to follow the pipeline
910 designed within our project, from acquisition of diverse berries, to extraction and analysis of biosynthetic
911 genes, to microbial fermentation-based production of specific polyphenols and running a biotech factory that
912 is exposed to real world situations, such as price fluctuations. The game is also available on iTunes1 and
913 Google Play2 store. Lastly, a 22 minute documentary film was produced, showing sampling of berries,
1 See: https://itunes.apple.com/us/app/berrymaker-dna-soda-factory/id1163115116?ls=1&mt=8
2 See: https://play.google.com/store/apps/details?id=com.biofaction.berrymaker
37
914 analysis of interesting compounds, and experiments with natural color pigments for food products from
915 across Europe (see: https://vimeo.com/193467652).
916 Conclusions
917 During three years of the project, the BacHBerry partners successfully established a complete pipeline for
918 screening, bioprospecting, identification, and microbial production of plant phenolic compounds in Gram-
919 positive bacteria. A summary of the most important results is presented in Table 5. The major highlights are
920 construction of the extensive germplasm collection database, establishment of a method for high-throughput
921 screening of bioactive extracts using the yeast-based human disease models, generation of a vast berry
922 transcriptome database, construction of microbial cell factories in L. lactis and C. glutamicum for the
923 production of polyphenols with yields comparable to those currently available from the literature, release of
924 several software packages for transcriptome analysis and in silico-based metabolic engineering, as well as
925 optimization of fermentation conditions and downstream processing for the selected organisms aimed at
926 increasing the yield and the purify of the final products. The pipeline has been completely validated and is
927 available to be picked up by the biotech industry for further improvement and commercialization. The
928 developed methods and protocols should be suitable for discovery, as well as production of most groups of
929 polyphenols in the above-mentioned organisms, without a need for significant alterations and adaptations.
930 Overall, this project provided the scientific community with enabling tools and resources to further
931 advancing research on berries, phenolic compounds, and metabolic engineering of microorganisms.
932 Additional efforts were also focused on communication with stakeholders, public outreach, and
933 popularization of science among the younger generation. The majority of the project outcomes is already
934 available to the scientific community and the industry in form of peer-reviewed articles, or will be published
935 within the coming year.
936
937 Acknowledgments
38
938 The authors would like to thank the European Union’s Seventh Framework Programme (BacHBerry, Project
939 No. FP7-613793, and FP7-PEOPLE-2013-COFUND, Project No. FP7-609405) for the financial support.
940 AD, RF, MHHN, PG, SGS, and JF would also like to acknowledge the Novo Nordisk Foundation. We
941 express our gratitude to Dr. Martha Cyert (Stanford School of Medicine, EUA), Dr. Hitoshi Shimoi (National
942 Research Institute of Brewing, Japan) and Dr. Yoshio Araki (Graduate School of Biosphere Science,
943 Hiroshima University, Japan) for providing the yeast strain YAA5. We also thank Dr. Ian Macraedie, RMIT
944 University, Australia) for providing the plasmid p416_GPDpr-GFP-A42.
945
946 Abbreviations
947 4CL, 4-coumaryl-CoA ligase; AD, Alzheimer's Disease; ANS, anthocyanidin synthase; ALS, amyotrophic
948 lateral sclerosis; anthocyanidin synthase; ANR, anthocyanidin reductase; C3’H, p-coumaroyl
949 shikimate/quinate 3'-hydroxylase; C4H, cinnamate 4-hydroxylase; CBD, Convention on Biological
950 Diversity; CaN, calcineurin; CDRE, Calcineurin-Dependent Responsive Element; CHI, chalcone isomerase;
951 CHR, chalcone reductase; CHS, chalcone synthase; CPR, cytochrome-P450 reductase; CVs, column
952 volumes; CYP, cytochromes P450; DFR, dihydroflavonol 4-reductase; F3H, flavanone 3-hydroxylase; F3’H,
953 flavonoid 3’-hydroxylase; F3’5’H, flavonoid 3’-5’-hydroxylase; FLS, flavonol synthase; FNS, flavone
954 synthase; FP7, European Commission’s the Seventh Framework programme; GFP, Green Fluorescent
955 Protein; GRAS, generally regarded as safe; HCT, hydroxycinnamoyl-CoA shikimate/quinate
956 hydroxycinnamoyltransferase; HD, Huntington's Disease; HPLC, high-performance liquid chromatography;
957 LAR, leucoanthocyanidin reductase; LC-MS, liquid-chromatography mass-spectrometry; NDs,
958 Neurodegenerative diseases; NFAT, Nuclear Factor of Activated T-cells; NFκB, Nuclear Factor κB; NP-
959 ABS, The Nagoya protocol on access to genetic resources and the fair and equitable sharing of benefits
960 arising from their utilization; OMT, O-methyltransferase; PAL, phenylalanine ammonia-lyase; PCA,
961 principal component analysis; PD, Parkinson's Disease; PLS, partial least squares; RNA-seq, RNA
39
962 sequencing technology; STS, stilbene synthase; SVR, support vector regression; TAL, tyrosine ammonia-
963 lyase; UFGT, flavonoid 3-O-glucosyltransferase.
964
965 Appendices
966 Supplementary_Materials.docx
967 Supplementary_Tables.docx
968
969 References
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1409
1410
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1411 Figure legends
1412 Figure 1: a) The molecular structure of the stilbene trans-resveratrol. b) Basic flavonoid structure. Rx are
1413 positions where decorating groups (e.g. hydroxyl, acyl, glycosyl, etc.), which differentiate the compounds
1414 and alter their chemical properties, could be attached.
1415 Figure 2: Principal component analysis (PCA) plots of the full dataset without reference material and
1416 blanks. Each dot represents the profile of one sample aliquot whereas lines delimit groups of samples that
1417 cluster together. a) PCA plots of the full dataset without reference material and blanks in positive mode.
1418 Components (Scr) 1 and 2 explain 9.79% and 7.59% of the variation respectively. b) PCA plots of the full
1419 dataset without reference material and blanks in negative mode. Components (Scr) 1 and 2 explain 13.20%
1420 and 10.30% of the variation respectively.
1421 Figure 3: Schematic representation of yeast models. a. Yeast models of neurodegenerative diseases.
1422 Humanized yeast strains encoding chimeric fusions of disease genes with GFP (green fluorescent protein)
1423 under the control of a galactose-inducible (GAL1) promoter. Expression of Aβ42, αSyn (SNCA gene), FUS
1424 and HTTpQ103 leads to the formation of toxic aggregates, recapitulating the pathological processes of
1425 Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Huntington’s
1426 disease (HD), respectively. b. Yeast model of Crz1 (NFAT orthologue) activation. Crz1 is activated by the
1427 serine/threonine protein phosphatase calcineurin (composed by CnA and CnB subunits). The reporter strain
1428 encodes the lacZ gene under the control of a promoter containing Crz1-binding sites, allowing the
1429 assessment of Crz1 activation through the measurement of β-galactosidase activity.
1430 Figure 4: Identification of bioactivities in the berry germplasm. a) Strategy used for the discovery of
1431 bioactive compounds for Alzheimer’s disease. BY4741_erg6 recombinant yeast cells expressing GFP-Aβ42
1432 were grown in SD galactose medium and cell viability was assessed by growth curves for 24 h in cells
1433 challenged or unchallenged with Lycium chinense polyphenol extracts. Cells containing the empty vector
1434 were used as controls. The areas under the curve (AUC) were integrated using the Origin software
61
1435 (OriginLab, Northampton, MA). Values represent the mean ± standard error of mean (SEM) of at least three
1436 biological replicates.
1437 b) Strategy used for the discovery of bioactive compounds with anti-inflammatory potential. BY4741 yeast
1438 cells encoding CDREpromoter-lacZ were treated or not with Lycium chinense polyphenol extracts and induced
1439 with 1.8 mM MnCl2. Crz1 activation was assessed by monitoring β-galactosidase activity using ortho-
1440 nitrophenyl-β-galactoside (ONPG). The immunosuppressant FK506, a well-known inhibitor of calcineurin
1441 (Dumont 2000) was used as a positive control. The values represent the mean ± SEM of at least three
1442 biological replicates, *p < 0.05, **p < 0.01, ***p < 0.001.
1443 Figure 5. Yeast as a discovery platform of therapeutic compounds. Iterative bio-guided fractionation of
1444 complex mixtures allows the discovery of small molecules improving cell growth, by rescue of protein
1445 aggregates toxicity, or inhibiting inflammatory processes. High-throughput formats aligned with chemical
1446 identification by LC-MS approaches accelerate the identification of candidate compounds.
1447 Figure 6: Polyphenol biosynthetic pathway in plants (modified from Falcone Ferreyra et al. 2012). PAL,
1448 phenylalanine ammonia-lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-coumaroyl-CoA ligase; CHS,
1449 chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3’5’H, flavonoid 3’-5’-
1450 hydroxylase; DFR, dihydroflavonol 4-reductase; ANS, anthocyanidin synthase; ANR, anthocyanidin
1451 reductase; UFGT, flavonoid 3-O-glucosyltransferase. LAR, leucoanthocyanidin reductase; FNS, flavone
1452 synthase; FLS, flavonol synthase; STS, stilbene synthase. P450 enzymes are highlighted in red.
1453 Figure 7: Overview of the endogenous phenylpropanoid degradation and the engineered pathway for
1454 polyphenol synthesis in C. glutamicum. One of the precursors for the production of the stilbene trans-
1455 resveratrol (catalyzed by stilbene synthase, STS) or for naringenin chalcone (catalyzed by chalcone
1456 synthases, CHS) is p-coumaroyl-CoA. Naringenin chalcone isomerizes to the (2S)-flavanone naringenin
1457 either spontaneously or catalyzed by the activity of chalcone isomerase (CHI). In C. glutamicum, p-coumaric
1458 acid can be degraded to 4-hydroxybenzoate by a CoA-dependent, β-oxidative deacetylation pathway. 4-
1459 Hydroxybenzoate is subsequently hydroxylated to protocatechuate, which is catabolized to succinyl-CoA
62
1460 and acetyl-CoA by the β-ketoadipate pathway. Genes coding for the underlined enzymes were deleted in the
1461 course of the construction of the platform strain C. glutamicum DelAro4.
1462 Figure 8: Heterologous biosynthetic pathway for fisetin assembled in E. coli. TAL, tyrosine ammonia-lyase;
1463 4CL, 4-coumaroyl-CoA ligase; CHS::CHR, chalcone synthase::chalcone reductase fusion; CHI, chalcone
1464 isomerase; F3H, flavanone 3-hydroxylase; FLS, flavonol synthase; F3’H::CPR, flavonoid 3’-
1465 hydroxylase::cytochrome P450 reductase fusion.
1466 Figure 9: Heterologous biosynthetic pathways introduced into the genome-scale models of C. glutamicum
1467 and L. lactis leading to the four target compounds: i) trans-resveratrol ii) pelargonidin iii) quercetin and iv)
1468 fisetin. Two heterologous enzymes (TAL and 4CL) are common for the biosynthesis of the phenolic
1469 molecules. Enzyme abbreviations: TAL, tyrosine ammonia-lyase; 4CL, 4-coumaroyl-CoA ligase; STS,
1470 stilbene synthase; CHS, chalcone synthase; CHR, chalcone reductase; CHI, chalcone isomerase; F3H,
1471 flavanone 3–hydroxylase; FLS, flavonol synthase; F3’H, flavonoid 3' hydroxylase; CPR, cytochrome P450
1472 reductase.
1473 Figure 10: Partition coefficients in the form of log P values of trans-resveratrol using different organic
1474 solvents using C. glutamicum fermentation broth as the aqueous phase.
1475 Figure 11: On the left, protein concentration (expressed as percentage of the initial broth protein
1476 concentration) in the organic and aqueous phases, after performing liquid extraction. The concentration in
1477 the organic phase is almost zero (considering the error bars), indicating that some protein stayed in the
1478 interphase. On the right, the formation of an interphase after the broth had been extracted with organic
1479 solvent (a mixture of heptanoic acid and hexyl acetate in this case) was clear.
1480 Figure 12: Chromatogram following a pulse injection of 10 ml of C. glutamicum spent fermentation broth in
1481 a 30 ml column packed with Amberlite XAD-16 resin. A washing step was performed with 10 column
1482 volumes (CVs) of MilliQ water. Afterwards, elution was performed with a water/ethanol gradient for 12 CV,
1483 followed by a final regeneration step with 100% ethanol.
63
1484 Figure 13: Experimental set-up for the “Continuous Product Removal” mode.
1485
64
1486 Tables
1487 Table 1: Anthocyanidin structures (aglycone form) found in different berry species. The presence of a
1488 compound is noted with + whereas the absence is noted with -.
Type Pelargonidin Cyanidin Delphinidin Peonidin Petunidin Malvinidin
Structure
Molecular + + + + + + C15H11O5 C15H11O6 C15H11O7 C16H13O6 C16H13O7 C17H15O7 formula
Fragaria1 + + - - - -
Ribes1 + + + - + -
Rubus1 + + - - - -
Vaccinium1 - + + + + +
Vitis1 - + + + + +
Lonicera2,5 + + + + - +
Lycium3 - - + - + +
Berberis4 - + + + + +
Ugni4 - + - + - -
Aristotelia6 - + + - - -
1489 1 Phenol explorer database (http://phenol-explorer.eu); 2 (Chaovanalikit et al. 2004); 3 (Zheng et al. 2011); 4
1490 (Ruiz et al. 2010); 5 (Wang et al. 2016b); 6 (Fredes et al. 2014)
1491
1492
65
1493 Table 2: Selected species for bioactivity testing according to the phytochemical diversity.
Species Variety/line Species Variety/line
Aristotelia chilensis - Rubus armeniacus Focke Himalayan Giant (Molina)Stuntz
Berberis buxfolia Lam. - Rubus occidentalis L. Huron
Lonicera caerulea L. S322-23 Rubus loganobaccus L.H.Bailey Sunberry
Lycium chinense Mill. - Rubus loganobaccus L.H.Bailey Tayberry
Ribes grossularia L. 9294 Rubus bartonii Newton Ashton Cross
Ribes grossularoides Maxim. H1-12-1 Rubus fruticosus L. agg. Fantasia
Ribes nevadense Kellog - Rubus brigantinus Samp. -
Ribes spp. 644217 Rubus genevierii Boreau. -
Ribes spp. 1126 Rubus henriquesii Samp. -
Ribes spp. 2292-1 Rubus hochstetterorum Seub. -
Rubus sampaioanus Sudre ex Ribes spp. Muravushka - Samp.
Ribes spp. - Rubus vagabundus Samp. -
Ribes petraeum Wulf. R2-1-2 Rubus idaeus L. Octavia
Ribes x saundersii Jancz. XB4 Rubus idaeus L. Prestige
Ribes bethmontii Jancz. XW6 Ugni molinae Turcz. Red Pearl
Ribes rubrum L. S11-3-36 Vaccinium uliginosum L. -
Rubus spp. B14 Vaccinium vitis-idaea L. -
1494
1495
66
1496 Table 3: Transcriptome resources for berries developed on BacHBerry
Species Plant material Total number of raw reads
Aristotelia sp. ripe berries 397,707,372
Berberis sp. ripe berries 444,362,698
Corema sp. leaf 353,604,932
Lonicera sp. ripe berries 397,214,254
Ribes sp.1 ripe berries 336,479,242
Ribes sp.2 ripe berries 393,665,630
Rubus sp.1 berries (3 stages) 1,040,224,680
Rubus sp.2 berries (3 stages) 1,064,858,518
Rubus sp.3 ripe berries 505,754,030
Rubus sp.4 ripe berries 390,608,452
Ugni sp. ripe berries 405,024,920
Vaccinium sp.1 ripe berries 373,159,882
Vaccinium sp.2 ripe berries 375,778,718
1497
1498
67
1499 Table 4: Statistics on five major enzyme families representing conserved biochemical steps of the
1500 phenylpropanoid pathway. Candidates displayed more than 70% sequence coverage to reference query
1501 sequences. This work led to the creation of a large dataset of candidates used for pathway elucidation and
1502 reconstitution in bacterial hosts.
Enzyme families Candidates per transcriptome (min-max)
Conserved phenylpropanoid pathway enzymes 109-180
CYPs 100-177
UGTs 70-145
2-OGDs 32-88
O-methyltransferases 8-51
Acyltransferases 23-36
1503
1504
68
1505 Table 5: Summary of the outcomes of the project
Specific Bioprospecting for discovery of Characterization of phenolics Bacterial cell factories for Fermentation and application novel berry phenolics biosynthetic pathways production of berry phenolics bioprocess engineering area in project General aims To tap into the biodiversity of berries Identification and characterization Design and generation of bacterial Implementation of production of high- of BacHBerry from around the globe and of their of berry phenolics biosynthetic cell factories for production of high- value phenolics in fermenters up to phenolics content pathways value berry phenolics demonstration scale Achievements • Standardization of methodologies • Transcriptomes of 13 germplasms • L. lactis strains were engineered for • Cultivation conditions for improved of the project for harvesting, extract preparation, spanning eight genera, seven stilbene (trans-resveratrol and productivity were developed for both phenolics content assessment and families and seven orders were pterostilbene) and anthocyanin L. lactis and C. glutamicum through fractionation generated and analyzed production optimization of fermentation parameters (batch versus fed-batch • Metabolomics data from berry •New algorithms for functional • Production of various stilbenes operation, substrate and dissolved species originating from UK, genomics and improved (trans-resveratrol, pinosylvin, and oxygen concentration, etc.) mainland Europe, Russia, Chile, and computational methods for piceatannol), methylated stilbenes China was obtained and made pathway identification were (pinostilbene and pterostilbene), • Whey was evaluated as an alternative available in form of an online established flavanones (naringenin, pinocembrin, carbon source for polyphenol database and eryodictiol), and flavonols production in L lactis • Over 4000 candidate genes for (kaempferol and quercetin) was • Implementation of robust assays various biosynthetic steps, achieved in C. glutamicum with • Multiple bioseparation methods for based on the SMART platform for transport and regulation of yields comparable to those obtained extraction of produced polyphenols discovery of bioactivity and polyphenol production were in the model organism E. coli. from fermentation broth were functionality identified Production titers for the produced evaluated
69
flavonols are the highest titers • Obtained berry extracts were • Biochemical activities of some of obtained in engineered • Two possible designs for a system for assayed for bioactivity against the gene candidates were tested, microorganism until today continuous product removal from the several human diseases, and thus validating the predictions fermentation broth were implemented additionally evaluated for other • Polyphenol production was further functional uses (eq. food additives, improved by engineering enhanced • Resveratrol production in C.
antimicrobials). Bioactivities against precursor supply (eq. L-tyrosine, glutamicum was scaled up from shake Alzheimer’s, Parkinson’s and malonyl-CoA) via rational design, flask to 5 l fermenter to demonstration Huntington’s diseases, Amyotrophic modeling-based prediction, and use scale (250 l) Lateral Sclerosis and inflammation of biosensors were detected. • Alternative biosynthetic routes were • Several pure bioactive effector explored though engineering of compounds were identified and trans-resveratrol production from 4- validated hydroxybenzoic acid (HBA) in C. glutamicum
• Computational models were developed allowing predicting a minimal set of modifications needed for improved polyphenol production in both a single-strain population and a consortium of two or more species 1506
70
1507
71
Figure 1: a) The molecular structure of the stilbene trans-resveratrol. b) Basic flavonoid structure. Rx are positions where decorating groups (e.g. hydroxyl, acyl, glycosyl, etc.), which differentiate the compounds and alter their chemical properties, could be attached.
Figure 2: Principal component analysis (PCA) plots of the full dataset without reference material and blanks. Each dot represents the profile of one sample aliquot whereas lines delimit groups of samples that cluster together. a) PCA plots of the full dataset without reference material and blanks in positive mode. Components (Scr) 1 and 2 explain 9.79% and 7.59% of the variation respectively. b) PCA plots of the full dataset without reference material and blanks in negative mode. Components (Scr) 1 and 2 explain 13.20% and 10.30% of the variation respectively.
Figure 3: Schematic representation of yeast models. a. Yeast models of neurodegenerative diseases. Humanized yeast strains encoding chimeric fusions of disease genes with GFP (green fluorescent protein) under the control of a galactose-inducible (GAL1) promoter. Expression of Aβ42, αSyn (SNCA gene), FUS and HTTpQ103 leads to the formation of toxic aggregates, recapitulating the pathological processes of Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD), respectively. b. Yeast model of Crz1 (NFAT orthologue) activation. Crz1 is activated by the serine/threonine protein phosphatase calcineurin (composed by CnA and CnB subunits). The reporter strain encodes the lacZ gene under the control of a promoter containing Crz1-binding sites, allowing the assessment of Crz1 activation through the measurement of β-galactosidase activity.
Figure 4: Identification of bioactivities in the berry germplasm. a) Strategy used for the discovery of bioactive compounds for Alzheimer’s disease. BY4741_erg6 recombinant yeast cells expressing GFP-Aβ42 were grown in SD galactose medium and cell viability was assessed by growth curves for 24 h in cells challenged or unchallenged with Lycium chinense polyphenol extracts. Cells containing the empty vector were used as controls. The areas under the curve (AUC) were integrated using the Origin software (OriginLab, Northampton, MA). Values represent the mean ± standard error of mean (SEM) of at least three biological replicates. b) Strategy used for the discovery of bioactive compounds with anti-inflammatory potential. BY4741 yeast cells encoding CDREpromoter-lacZ were treated or not with Lycium chinense polyphenol extracts and induced with 1.8 mM MnCl2. Crz1 activation was assessed by monitoring β-galactosidase activity using ortho- nitrophenyl-β-galactoside (ONPG). The immunosuppressant FK506, a well-known inhibitor of calcineurin (Dumont 2000) was used as a positive control. The values represent the mean ± SEM of at least three biological replicates, *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 5. Yeast as a discovery platform of therapeutic compounds. Iterative bio-guided fractionation of complex mixtures allows the discovery of small molecules improving cell growth, by rescue of protein aggregates toxicity, or inhibiting inflammatory processes. High-throughput formats aligned with chemical identification by LC-MS approaches accelerate the identification of candidate compounds.
Figure 6: Polyphenol biosynthetic pathway in plants (modified from Falcone Ferreyra et al. 2012). PAL, phenylalanine ammonia-lyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-coumaroyl-CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3’5’H, flavonoid 3’-5’- hydroxylase; DFR, dihydroflavonol 4-reductase; ANS, anthocyanidin synthase; ANR, anthocyanidin reductase; UFGT, flavonoid 3-O-glucosyltransferase. LAR, leucoanthocyanidin reductase; FNS, flavone synthase; FLS, flavonol synthase; STS, stilbene synthase. P450 enzymes are highlighted.
protocatechuate 3-carboxy-cis,cis-muconate O O O - tricarboxylic - PcaGH HO HO O O acid cycle glucose HO HO O shikimate PcaD pathway PobA PcaC succinyl-CoA PcaB - O O O PcaIJ O - SCoA - O O PcaF OH O - O O O HO NH O + 2 L-tyrosine HO SCoA O 3-oxoadipate TAL PhdE 4-hydroxybenzoate acetyl-CoA PhdB O PhdC OH acid HO p-coumaric O CHS O OH 4CL SCoA HO + 3 malonyl-CoA p-coumaroyl-CoA HO HO OH naringenin CHI STS + 3 malonyl-CoA chalcone
O OH OH
O OH
OH HO HO resveratrol naringenin
Figure 7: Overview of the endogenous phenylpropanoid degradation and the engineered pathway for polyphenol synthesis in C. glutamicum. One of the precursors for the production of the stilbene trans- resveratrol (catalyzed by stilbene synthase, STS) or for naringenin chalcone (catalyzed by chalcone synthases, CHS) is p-coumaroyl-CoA. Naringenin chalcone isomerizes to the (2S)-flavanone naringenin either spontaneously or catalyzed by the activity of chalcone isomerase (CHI). In C. glutamicum, p-coumaric acid can be degraded to 4-hydroxybenzoate by a CoA-dependent, β-oxidative deacetylation pathway. 4- Hydroxybenzoate is subsequently hydroxylated to protocatechuate, which is catabolized to succinyl-CoA and acetyl-CoA by the β-ketoadipate pathway. Genes coding for the underlined enzymes were deleted in the course of the construction of the platform strain C. glutamicum DelAro4.
Figure 8: Heterologous biosynthetic pathway for fisetin assembled in E. coli. TAL, tyrosine ammonia-lyase; 4CL, 4-coumaroyl-CoA ligase; CHS::CHR, chalcone synthase::chalcone reductase fusion; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; FLS, flavonol synthase; F3’H::CPR, flavonoid 3’- hydroxylase::cytochrome P450 reductase fusion.
Figure 9: Heterologous biosynthetic pathways introduced into the genome-scale models of C. glutamicum and L. lactis leading to the four target compounds: i) trans-resveratrol ii) pelargonidin iii) quercetin and iv) fisetin. Two heterologous enzymes (TAL and 4CL) are common for the biosynthesis of the phenolic molecules. Enzyme abbreviations: TAL, tyrosine ammonia-lyase; 4CL, 4-coumaroyl-CoA ligase; STS, stilbene synthase; CHS, chalcone synthase; CHR, chalcone reductase; CHI, chalcone isomerase; F3H, flavanone 3–hydroxylase; FLS, flavonol synthase; F3’H, flavonoid 3' hydroxylase; CPR, cytochrome P450 reductase.
Figure 10: Partition coefficients in the form of log P values of trans-resveratrol using different organic solvents using C. glutamicum fermentation broth as the aqueous phase.
Figure 11: On the left, protein concentration (expressed as percentage of the initial broth protein concentration) in the organic and aqueous phases, after performing liquid extraction. The concentration in the organic phase is almost zero (considering the error bars), indicating that some protein stayed in the interphase. On the right, the formation of an interphase after the broth had been extracted with organic solvent (a mixture of heptanoic acid and hexyl acetate in this case) was clear.
Figure 12: Chromatogram following a pulse injection of 10 ml of C. glutamicum spent fermentation broth in a 30 ml column packed with Amberlite XAD-16 resin. A washing step was performed with 10 column volumes (CVs) of MilliQ water. Afterwards, elution was performed with a water/ethanol gradient for 12 CV, followed by a final regeneration step with 100% ethanol.
Figure 13: Experimental set-up for the “Continuous Product Removal” mode.
SUPPLEMENTARY TABLES
Title
BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits
Running Title
BacHBerry
Authors
BacHBerry consortium
*Corresponding author:
Alexey Dudnik
Email: [email protected]
Phone: + 45 93 51 11 01
Fax: +45 45 25 80 01
Table S1 – Decorating glycosyl groups of anthocyanins found in different berry species. The presence of a glycosylation pattern is noted with + whereas the absence is noted with -.
- - - O - 5
- O -
- O 5
-
- 5
- rutinoside
- rutinoside
arabinoside glucoside galactoside rutinoside sambubioside sophoroside glucosyl sambubiosyl galactoside gentibioside rutinoside diglucoside ------xyloside - - - xylosyl - - - - - O - O - O - O - O - O - O - O - O - O - O - O - O 3 3 3 3 3 3 3 3,5 3 3 glucoside 3 3 glucoside 3 3 glucoside Fragaria1 + + - + ------Ribes1 - + + + + + - - + - + - - - Rubus1 - + - + - + + + + - + - - - Vaccinium1 + + + ------Vitis1 - + ------Lonicera2 - + - + - - - + - - - - + - Lycium3 ------+ - - - + - + Berberis4,5 - + - + + - - + - + - - - + Ugni4 - + ------1 Phenol explorer database (http://phenol-explorer.eu) 2 Chaovanalikit et al., 2004 3 Zheng et al., 2011 4 Ruiz et al., 2010 5 Ruiz et al., 2013
Table S2 – Decorating acyl groups of anthocyanins found in in a variety of berry species
- - - glycoside) glycoside) - - - glycoside) - glycoside)
- coumaryl
(6’’acetyl (6’’malonyl (6’’succinnyl (6’’dioxyalyl (feruloyl (6’’caffeoyl - - - - - (6’’p - -
- O - O - O - O - O - O - O 3 3 3 glycoside) 3 glycoside) 3 glycoside) 3 3
1 Fragaria - + + - - - - 1 Ribes - - - - + - + 1 Rubus - + - + - - - 1 Vaccinium + ------Vitis1,2 + - - - + - + Lycium3 - - - - + + +
1 Phenol explorer database (http://phenol-explorer.eu) 2 Figueiredo-Gonzalez et al. 2012 3 Zheng et al. 2011
Table S3 – List of all the species/varieties and harvest locations of the germplasm collections
Species Variety/line Harvest location Aristotelia chilensis (Molina)Stuntz - Chilei Berberis buxfolia Lam. - Chileii Lonicera caerulea L. 260-11 Russiaiii Lonicera caerulea L. 838-12 Russia Lonicera caerulea L. Avacha Russiaiii Lonicera caerulea L. Bazhovskaya Russiaiii Lonicera caerulea L. Leningradskii Velikan Russiaiii Lonicera caerulea L. Malvina Russiaiii Lonicera caerulea L. Morena Russiaiii Lonicera caerulea L. Nimfa Russiaiii Lonicera caerulea L. Pavlovskaya Russiaiii Lonicera caerulea L. S322-23 Russiaiii Lonicera caerulea L. Solovey Russiaiii Lonicera caerulea L. Start Russiaiii Lonicera caerulea L. Suvenir Russiaiii Lycium chinense Mill. - Chinaiv Ribes spp. L. 1126 United Kingdomv Ribes spp. L. 1196 United Kingdomv Ribes spp. L. 2174 United Kingdomv Ribes spp. L. 2292-1 United Kingdomv Ribes spp. L. 644217 United Kingdomv Ribes spp. L. - United Kingdomv Ribes spp. L. - Chileii Ribes aureum Pursh R2-1-2 United Kingdomv Ribes bethmontii Jancz. XW6 United Kingdomv Ribes distans Jancz. 2269 United Kingdomv Ribes divaricatum Dougl. Montana United Kingdomv Ribes fuscescens Jancz. XB6 United Kingdomv Ribes futurum Jancz. XR4 United Kingdomv Ribes giraldii Jancz. B2-3-1 United Kingdomv Ribes grossularia L. 1815-21 United Kingdomv Ribes grossularia L. 1824-37 United Kingdomv Ribes grossularia L. 9492-5 United Kingdomv Ribes grossularia L. 96101-1 United Kingdomv Ribes grossularoides Maxim. H1-12-1 United Kingdomv Ribes holosericeum Otto& A.Dietr. XR2 United Kingdomv Ribes irriguum Douglas H1-5-1 United Kingdomv Ribes koehneanum Jancz. XR3 United Kingdomv Ribes nevadense Kellog - United Kingdomv Ribes nigrum L. Ben Alder United Kingdomv Ribes nigrum subsp. europaeum х R. nigrum Almiai Russiaiii subsp. sibiricum x R.dikuscha Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Azhurnaya Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Bolero Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum Buraya Dal’nevostochnaya Russiaiii subsp. sibiricum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Charovnitsa Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum Dochka Russiaiii subsp. sibiricum x R.dikuscha Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Krasa L’vova Russiaiii subsp. scandinavicum R.nigrum subsp. scandinavicum x R. Lazur’ Russiaiii dikuscha Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Malen’kii Prints Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Mila Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Monisto Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Muravushka Russiaiii subsp. scandinavicum x R.janczewskii Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Ocharovanie Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Orlovskaya serenada Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Orlovskii Val’s Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum Sensei Russiaiii subsp. sibiricum x R.dikuscha Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Sharovidnaya Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum Syuita Kievskaya Russiaiii subsp. sibiricum x R.dikuscha Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.nigrum subsp. Ukrainka Russiaiii scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Veloi Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Vospominanie Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum subsp. sibiricum x R.dikuscha x R.nigrum Zelenaya Dymka Russiaiii subsp. scandinavicum Ribes nigrum subsp. europaeum х R. nigrum Zhuravushka Russiaiii subsp. sibiricum x R.dikuscha Ribes petraeum Wulf. R2-1-2 United Kingdomv Ribes rotundifolium Michx. H1-3-1 United Kingdomv Ribes rubrum L. Red Poll United Kingdomv Ribes rubrum L. 1518 United Kingdomv Ribes rubrum L. 1169 United Kingdomv Ribes rubrum L. 1196-2 United Kingdomv Ribes rubrum L. 1757-2 United Kingdomv Ribes rubrum L. Red Wing United Kingdomv Ribes rubrum L. S11-3-36 United Kingdomv Ribes sativum Syme R7-3-3 United Kingdomv Ribes x saundersii Jancz. XB4 United Kingdomv Ribes succirubrum Zabel ex Jancz. XW8 United Kingdomv Ribes warcewiczii Jancz. R7-3-3 United Kingdomv Ribes watsonianum Koehne - United Kingdomv Rubus armeniacus Focke [= R. procerus Himalayan Giant United Kingdomv auct.]) Rubus bartonii Newton syn. ‘Ashton Cross’ Ashton Cross United Kingdomv Rubus fruticosus L. agg. B13 United Kingdomv Rubus fruticosus L. agg. B14 United Kingdomv Rubus fruticosus L. agg. Fantasia United Kingdomv Rubus fruticosus L. agg. Karaka Black United Kingdomv Rubus geoides Sm - Chileii Rubus ursinus x R. armeniacus x R. idaeus Black Butte United Kingdomv R. ursinus x R.ursinus x R.idaeus x R. Kotata United Kingdomv ursinus x R. allegheniensis x R. argutus Rubus ursinus x R.ursinus x R.idaeus Ollalie United Kingdomv Rubus idaeus L. Glen Ample United Kingdomv Rubus idaeus L. Glen Fyne United Kingdomv Rubus idaeus L. Malling Admiral United Kingdomv Rubus idaeus L. Malmer Szedler United Kingdomv Rubus idaeus L. Octavia United Kingdomv Rubus idaeus L. Polana United Kingdomv Rubus idaeus L. Prestige United Kingdomv Rubus idaeus L. Tulameen United Kingdomv Rubus loganobaccus L.H.Bailey Nectarberry United Kingdomv Rubus loganobaccus L.H.Bailey Boysenberry United Kingdomv Rubus loganobaccus L.H.Bailey Loganberry United Kingdomv Rubus loganobaccus L.H.Bailey LY59-10 United Kingdomv Rubus loganobaccus L.H.Bailey Riwaka Choice United Kingdomv Rubus loganobaccus L.H.Bailey Sunberry United Kingdomv Rubus loganobaccus L.H.Bailey Tayberry United Kingdomv Rubus occidentalis L. Black Hawk United Kingdomv Rubus occidentalis L. Huron United Kingdomv Rubus occidentalis L. Plum Farmer United Kingdomv Rubus phoenicolasius Maxim. Wineberry Portugalvi Rubus brigantinus Samp. - Portugalvi Rubus genevieri Boreau. - Portugalvi Rubus henriquesii Samp. - Portugalvi Rubus hochstetterorum Seub. - Portugalvi Rubus sampaioanus Sudre ex Samp. - Portugalvi Rubus vagabundus Samp. - Portugalvi Ugni molinae Turcz. Red Pearl Chilevii Vaccinium angustifolium Ait. ex Benth Blomidon Chinaviii Vaccinium corymbosum L. Bluecrop Chinaix Vaccinium darrowii Camp Misty Chinaix Vaccinium uliginosum L. - Chinaviii Vaccinium virgatum Ait. Climax Chinaix Vaccinium vitis-idaea L. - Chinaviii
i Sampling location: Coyhaique, Región de Aysén, Chile ii Sampling location: Punta Arenas, Región de Magallanes, Chile iii Vavilov Institute collection: Pavlovsk, St-Petersburg, Russia iv Sampling location: Beijing botanical garden, Beijing, China v The James Hutton Institute collection: Invergowrie, Scotland, UK vi Propagation site: Fataca Experimental Field, Odemira, Portugal vii Sampling location:Mataquito, Región del Maule, Chile viii Sampling location: Tahe, Heilongjiang Province, China ix Sampling location: Dalian, Liaoning Province, China SUPPLEMENTARY MATERIALS
Title
BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits
Running Title
BacHBerry
Authors
BacHBerry consortium
*Corresponding author:
Alexey Dudnik
Email: [email protected]
Phone: + 45 93 51 11 01
Fax: +45 45 25 80 01
S1: Materials and Methods
Plant material and extraction procedure: A total of 112 different cultivars and species from the Aristotelia, Berberis, Lonicera, Lycium, Ribes, Rubus, Ugni and Vaccinium genera grown in different locations (see Supplementary Table 1) were harvested manually in the field and kept under cool conditions until they were transferred to -20ºC storage. Frozen fruit of each species/cultivar was weighed (approximately 50 g) into a solvent-proof blender containing 150 ml of pre-cooled 50ng ml-1 Morin (Sigma-Aldrich, Gillingham, UK) solution prepared with 0.2% formic acid in methanol solution. Samples were then homogenized and subsequently filtered using Whatman filter paper grade 1. The resulting filtrate was aliquoted and solvent-dried using a speed-vac followed by lyophilization.
Dried extracts were flushed with N2 and stored at -20ºC until analysis by LC-ToF-MS.
Phenolic profile determination by LC-ToF-MS: Dried extracts from each species/cultivar were resolubilized in triplicate using 2 ml of a 75% methanol solution with 0.1% formic acid. From these extracts, 500 µl were decanted into filter vials, sealed with 0.45 m PTFE-lined screwcap (Thomson Instrument Company, London, UK) and transferred into the autosampler. The analysis was achieved in positive and negative modes across 9 batches each, using an Agilent LC-ToF-MS system (Agilent Technologies, Cheadle, Cheshire, UK) consisting of a quaternary pump (Agilent 1260), a diode-array- detector (DAD) (Agilent 1260) a temperature control device (Agilent 1260) and a Thermostat (Agilent 1290) coupled to an Agilent 6224 time-of-flight (ToF) instrument. Five microliters of the sample were injected onto a 2x150 mm (4 µm) C18 column fitted with a C18 4 x 2 mm Security Guard™ cartridge (Phenomenex, Torrance, CA, USA). Sample and column temperatures were maintained at 4ºC and 30ºC, respectively. The samples were eluted at a flow rate of 0.3 ml min-1 using two mobile phases (A: 0.1% Formic acid in dH2O; B: 0.1% formic acid in 50:50 dH2O: Acetonitrile) with the following gradient: 0 min 5%B; 4 min 5% B; 32.00 min 100%B; 34.00 min 100% B; 36.00 min5% B; 40.00 min 5% B. For optimal electrospray ionization conditions the nebulizer pressure, drying gas temperature and drying gas were set to 45 psi, 350ºC and 3 l min-1, respectively. In addition the diode array detection (DAD) was performed at 254, 280 and 520 nm. Morin levels (internal standard) were integrated in Agilent Mass Hunter Quan software (v. B.06.00) and all the samples with deviations larger than 10% relative to the dataset mean were reinjected. For all samples, three aliquots were analysed across three different analytical batches.
Component detection, peak alignment and integration: All chromatograms were processed identically using the Agilent Software Profinder v B.06.00 (Agilent Technologies, Cheadle, Cheshire, UK) which combines peak finding and integration algorithms for high-throughput data generation. For positive and negative mode data, the batch recursive molecular feature was used with peak extraction restricted to 2.1-38.00 mins of the chromatography and a peak threshold set at 15000 counts. The algorithm was set-up to include potential adducts of +H, +Na+, +K+ and +NH4+ in positive mode and - H and +Cl- in negative mode restricting the compounds to a maximum of one charge state. The compound ion count threshold was set at two or more ions, and for alignment purposes the RT window was set at 0.70% + 0.60 mins and the mass window was set at 25 ppm + 2 mDa. A post- processing filter to restrict analysis to compounds with more than 15000 counts and present in at least 3 of the files in at least one sample group (species/line). The find-by-ion options were set to limit the extracted ion chromatogram (EIC) to the expected retention time +/- 0.40 minutes. The “Agile” algorithm was used for the integration of EIC, with a gaussian smoothing of 9 points applied before the integration with a Gaussian width of 3 points. Additionally, peak filters were set at over 15000 counts and the chromatogram formats were set to centroid when available and otherwise profile. Spectrum was extracted at 10% of peak height and excluded if the spectra within the m/z range used was above 20% of saturation. Finally a post-processing filter was applied and compounds with less than 15000 counts or present in less 3 files in at least one sample group (species/line) were excluded.
Statistical analyses: Statistical analyses of all data were performed using GenStat for Windows, 16th Edition (VSN international Ltd., Hemel Hempstead, UK). A principal component analysis (PCA), based on the correlation matrix was applied to all the QC samples to ensure that the blank, reference samples, and berry samples were well separated (data not shown). Two separate analysis of the metabolomics dataset on the berry samples were carried out for the positive and negative dataset. A PCA, using the correlation matrix, was used to generate PCA plots of the first 4 principal components, which were subsequently used for selecting the species samples providing the most separation between samples.
Yeast plasmids, strains and transformation: p426_GAL1pr-GFP-Aβ42 was generated by inserting the sequence GFP-Aβ42, obtained by the double digestion of p416_GPDpr-GFP-Aβ42 (kindly provided by Ian Macraedie, RMIT University, AU) with BamHI/SmaI, into the p426 vector. p425_GAL1pr- GFP-Aβ42 was generated by subcloning the sequence GAL1pr-GFP-Aβ42 into the SacI/HindIII restriction sites of p425 vector. S. cerevisiae strains used in this study are BY4742 erg6 MATa his3Δ1 leu2Δ0 lys2Δ0 ura3Δ YBR082c::kanMX4 (EUROSCARF) and BY4742_CDRE-lacZ MATa his3 leu2 lys2 ura3 aur1::AUR1-C-4xCDRE-lacZ (Prescott et al, 2010).
Yeast growth conditions and extract treatments: Synthetic complete (SC) medium [0.67% (w/v) yeast nitrogen base without amino acids (YNB) (Difco) and 0.79 g l-1 complete supplement mixture (CSM) (QBiogene)], containing 2% (w/v) glucose was used for growth of Crz1-reporter strain. Crz1
activation was induced with 1.8 mM MnCl2 (Stathopoulos & Cyert). FK506 (Cayman Chemicals) was used as positive control. Synthetic dropout (SD)-LEU-URA media [0.67% (w/v) YNB and 0.54 g -1 l 6-amino acid dropout CSM-ADE-HIS-LEU-LYS-TRP-URA (QBiogene)], supplemented with standard concentrations of the required amino acids and containing 1% (w/v) raffinose, were used for growth of the AD yeast model. The repression or induction of disease protein expression was carried out in medium containing glucose (control, disease-protein OFF) and galactose (disease-protein ON) at a final concentration of 2% (w/v). A pre-inoculum was prepared in appropriated raffinose or glucose (only for Crz1-activation model) medium and cultures were incubated overnight at 30°C under orbital shaking. Cells were diluted in fresh medium and cultures were incubated under the same conditions until the optical density at 600 nm (OD600 nm) reached 0.5 ± 0.05 (log growth phase). To ensure synchronized cells cultures, cell suspensions were further diluted according to the equation ODi x Vi = (ODf/(2(t/gt)) x Vf, where ODi = initial optical density of the culture, Vi = initial volume of culture, ODf = final optical density of the culture, t = time (usually 16 h), gt = generation time of the strain and Vf = final volume of culture. Readings were performed in a 96-well microtiter plate using a Biotek Power Wave XS plate spectrophotometer.
Growth assays: Yeast cultures were diluted to OD600 nm 0.12 ± 0.012 in fresh medium supplemented or not with the indicated concentrations of extracts in a 96-well microtiter plate. After 2 h incubation
at 30ºC, cultures were further diluted to OD600 nm 0.03 ± 0.003 in medium containing glucose or galactose and supplemented or not with extracts. The cultures were then incubated at 30°C with shaking for 24 h or 48 h (for the AD model) and cellular growth was kinetically monitored hourly by measuring OD600 nm. The areas under the curve (AUC) were integrated using the Origin software (OriginLab, Northampton, MA).
β–Galactosidase assays: For quantitative measurements of β–galactosidase activity, OD600 nm of cells cultures were recorded just before cell lysis. Cells were then incubated with Y-PER Yeast Protein Extraction Reagent (ThermoFisher Scientific) in 96-well microtiter plates for 20 min at 37ºC, after -1 which LacZ buffer containing 4 mg l ONPG was added and plates incubated at 30ºC. The OD420 nm
and OD550 nm was periodically monitored using a Biotek Power Wave XS Microplate Spectrophotometer until the development of the yellow color. Miller units were calculated as described previously (Garcia et al, 2016).
Fractionation of bioactive fruit extracts: The freeze-dried extract of selected berries (eg Rubus genevieri, and Rubus idaeus) were re-suspended in Milli-Q water, filtered and adsorbed onto a semi- preparation column packed with a C18 resin (PREP C18 55-105 μm 125Å, Waters Co. USA) using an AKTA Explorer (GE USA). 0.1% Formic Acid (Sigma, USA) in Milli-Q water was chosen as mobile phase A and Methanol (Chromasolv purity≥99.9%, Sigma, USA) as mobile phase B. The approach was modified for the different berry extracts (Rubus genevieri, Rubus idaeus) to achieve proper fractionation, by tuning the hydrophobicity of the mobile phase (water-methanol). Modifying loading and elution mobile phase composition and gradient profile served to generate a maximum number of well identifiable peak/fractions for further bio-activity testing (ranging from 12 to 28 fractions). Fractions were brought under vacuum using a rapid-vac (Labconco, USA) to remove methanol for further bioactivity testing.
References
Garcia G, Santos CN do, Menezes R (2016) High-Throughput Yeast-Based Reporter Assay to Identify Compounds with Anti-inflammatory Potential. In: Matthiesen R (ed) Proteostasis. Springer, New York, USA, pp 441–452
Prescott TAK, Ariño J, Kite GC, Simmonds MSJ (2012) Inhibition of human calcineurin and yeast calcineurin-dependent gene expression by Jasminum humile leaf and root extracts. J Ethnopharmacol 140:293–297. doi: 10.1016/j.jep.2012.01.020